Understanding the land-atmospheric interaction in drought forecast from CFSv2 for the 2011 Texas and 2012 Upper Midwest US droughts

NASA Astrophysics Data System (ADS)

Zhang, Y.; Roundy, J. K.; Ek, M. B.; Wood, E. F.

2015-12-01

Prediction and thus preparedness in advance of hydrological extremes, such as drought and flood events, is crucial for proactively reducing their social and economic impacts. In the summers of 2011 Texas, and 2012 the Upper Midwest, experienced intense droughts that affected crops and the food market in the US. It is expected that seasonal forecasts with sufficient skill would reduce the negative impacts through planning and preparation. However, the forecast skill from models such as Climate Forecast System Version 2 (CFSv2) from National Centers for Environmental Prediction (NCEP) is low over the US, especially during the warm season (Jun - Sep), which restricts their practical use for drought prediction. This study analyzes the processes that lead to premature termination of 2011 and 2012 US summer droughts in CFSv2 forecast resulting in its low forecast skill. Using the North American Land Data Assimilation System version 2 (NLDAS2) and Climate Forecast System Reanalysis (CFSR) as references, this study investigates the forecast skills of CFSv2 initialized at 00, 06, 12, 18z from May 15 - 31 (leads out to September) for each event in terms of land-atmosphere interaction, through a recently developed Coupling Drought Index (CDI), which is based on the Convective Triggering Potential-Humidity Index-soil moisture (CTP-HI-SM) classification of four climate regimes: wet coupling, dry coupling, transitional and atmospherically controlled. A recycling model is used to trace the moisture sources in the CFSv2 forecasts of anomalous precipitation, which lead to the breakdown of drought conditions and a lack of drought forecasting skills. This is then compared with tracing the moisture source in CFSR with the same recycling model, which is used as the verification for the same periods. This helps to identify the parameterization that triggered precipitation in CFSv2 during 2011 and 2012 summer in the US thus has the potential to improve the forecast skill of CSFv2.

Extended-range ensemble forecasting of tropical cyclogenesis in the northern Indian Ocean: Modulation of Madden-Julian Oscillation

NASA Astrophysics Data System (ADS)

Fu, Xiouhua; Hsu, Pang-chi

2011-08-01

A conventional atmosphere-ocean coupled system initialized with NCEP FNL analysis has successfully predicted a tropical cyclogenesis event in the northern Indian Ocean with a lead time of two weeks. The coupled forecasting system reproduces the westerly wind bursts in the equatorial Indian Ocean associated with an eastward-propagating Madden-Julian Oscillation (MJO) event as well as the accompanying northward-propagating westerly and convective disturbances. After reaching the Bay of Bengal, this northward-propagating Intra-Seasonal Variability (ISV) fosters the tropical cyclogenesis. The present finding demonstrates that a realistic MJO/ISV prediction will make the extended-range forecasting of tropical cyclogenesis possible and also calls for improved representation of the MJO/ISV in contemporary weather and climate forecast models.

Verification of Advances in a Coupled Snow-runoff Modeling Framework for Operational Streamflow Forecasts

NASA Astrophysics Data System (ADS)

Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

2011-12-01

The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.

Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system.

PubMed

Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic

2014-06-28

The finite resolution of general circulation models of the coupled atmosphere-ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere-ocean climate system in operational forecast mode, and the latest seasonal forecasting system--System 4--has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981-2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden-Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific-North America region.

The Navy's First Seasonal Ice Forecasts using the Navy's Arctic Cap Nowcast/Forecast System

NASA Astrophysics Data System (ADS)

Preller, Ruth

2013-04-01

As conditions in the Arctic continue to change, the Naval Research Laboratory (NRL) has developed an interest in longer-term seasonal ice extent forecasts. The Arctic Cap Nowcast/Forecast System (ACNFS), developed by the Oceanography Division of NRL, was run in forward model mode, without assimilation, to estimate the minimum sea ice extent for September 2012. The model was initialized with varying assimilative ACNFS analysis fields (June 1, July 1, August 1 and September 1, 2012) and run forward for nine simulations using the archived Navy Operational Global Atmospheric Prediction System (NOGAPS) atmospheric forcing fields from 2003-2011. The mean ice extent in September, averaged across all ensemble members was the projected summer ice extent. These results were submitted to the Study of Environmental Arctic Change (SEARCH) Sea Ice Outlook project (http://www.arcus.org/search/seaiceoutlook). The ACNFS is a ~3.5 km coupled ice-ocean model that produces 5 day forecasts of the Arctic sea ice state in all ice covered areas in the northern hemisphere (poleward of 40° N). The ocean component is the HYbrid Coordinate Ocean Model (HYCOM) and is coupled to the Los Alamos National Laboratory Community Ice CodE (CICE) via the Earth System Modeling Framework (ESMF). The ocean and ice models are run in an assimilative cycle with the Navy's Coupled Ocean Data Assimilation (NCODA) system. Currently the ACNFS is being transitioned to operations at the Naval Oceanographic Office.

Improving wind energy forecasts using an Ensemble Kalman Filter data assimilation technique in a fully coupled hydrologic and atmospheric model

NASA Astrophysics Data System (ADS)

Williams, J. L.; Maxwell, R. M.; Delle Monache, L.

2012-12-01

Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its propensity to change speed and direction over short time scales. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. Using the PF.WRF model, a fully-coupled hydrologic and atmospheric model employing the ParFlow hydrologic model with the Weather Research and Forecasting model coupled via mass and energy fluxes across the land surface, we have explored the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture and wind speed, and demonstrated that reductions in uncertainty in these coupled fields propagate through the hydrologic and atmospheric system. We have adapted the Data Assimilation Research Testbed (DART), an implementation of the robust Ensemble Kalman Filter data assimilation algorithm, to expand our capability to nudge forecasts produced with the PF.WRF model using observational data. Using a semi-idealized simulation domain, we examine the effects of assimilating observations of variables such as wind speed and temperature collected in the atmosphere, and land surface and subsurface observations such as soil moisture on the quality of forecast outputs. The sensitivities we find in this study will enable further studies to optimize observation collection to maximize the utility of the PF.WRF-DART forecasting system.

Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system

PubMed Central

Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic

2014-01-01

The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific–North America region. PMID:24842026

Sea Ice in the NCEP Seasonal Forecast System

NASA Astrophysics Data System (ADS)

Wu, X.; Saha, S.; Grumbine, R. W.; Bailey, D. A.; Carton, J.; Penny, S. G.

2017-12-01

Sea ice is known to play a significant role in the global climate system. For a weather or climate forecast system (CFS), it is important that the realistic distribution of sea ice is represented. Sea ice prediction is challenging; sea ice can form or melt, it can move with wind and/or ocean current; sea ice interacts with both the air above and ocean underneath, it influences by, and has impact on the air and ocean conditions. NCEP has developed coupled CFS (version 2, CFSv2) and also carried out CFS reanalysis (CFSR), which includes a coupled model with the NCEP global forecast system, a land model, an ocean model (GFDL MOM4), and a sea ice model. In this work, we present the NCEP coupled model, the CFSv2 sea ice component that includes a dynamic thermodynamic sea ice model and a simple "assimilation" scheme, how sea ice has been assimilated in CFSR, the characteristics of the sea ice from CFSR and CFSv2, and the improvements of sea ice needed for future seasonal prediction system, part of the Unified Global Coupled System (UGCS), which is being developed and under testing, including sea ice data assimilation with the Local Ensemble Transform Kalman Filter (LETKF). Preliminary results from the UGCS testing will also be presented.

The Ensemble Space Weather Modeling System (eSWMS): Status, Capabilities and Challenges

NASA Astrophysics Data System (ADS)

Fry, C. D.; Eccles, J. V.; Reich, J. P.

2010-12-01

Marking a milestone in space weather forecasting, the Space Weather Modeling System (SWMS) successfully completed validation testing in advance of operational testing at Air Force Weather Agency’s primary space weather production center. This is the first coupling of stand-alone, physics-based space weather models that are currently in operations at AFWA supporting the warfighter. Significant development effort went into ensuring the component models were portable and scalable while maintaining consistent results across diverse high performance computing platforms. Coupling was accomplished under the Earth System Modeling Framework (ESMF). The coupled space weather models are the Hakamada-Akasofu-Fry version 2 (HAFv2) solar wind model and GAIM1, the ionospheric forecast component of the Global Assimilation of Ionospheric Measurements (GAIM) model. The SWMS was developed by team members from AFWA, Explorations Physics International, Inc. (EXPI) and Space Environment Corporation (SEC). The successful development of the SWMS provides new capabilities beyond enabling extended lead-time, data-driven ionospheric forecasts. These include ingesting diverse data sets at higher resolution, incorporating denser computational grids at finer time steps, and performing probability-based ensemble forecasts. Work of the SWMS development team now focuses on implementing the ensemble-based probability forecast capability by feeding multiple scenarios of 5 days of solar wind forecasts to the GAIM1 model based on the variation of the input fields to the HAFv2 model. The ensemble SWMS (eSWMS) will provide the most-likely space weather scenario with uncertainty estimates for important forecast fields. The eSWMS will allow DoD mission planners to consider the effects of space weather on their systems with more advance warning than is currently possible. The payoff is enhanced, tailored support to the warfighter with improved capabilities, such as point-to-point HF propagation forecasts, single-frequency GPS error corrections, and high cadence, high-resolution Space Situational Awareness (SSA) products. We present the current status of eSWMS, its capabilities, limitations and path of transition to operational use.

Ocean-Atmosphere Coupling Processes Affecting Predictability in the Climate System

NASA Astrophysics Data System (ADS)

Miller, A. J.; Subramanian, A. C.; Seo, H.; Eliashiv, J. D.

2017-12-01

Predictions of the ocean and atmosphere are often sensitive to coupling at the air-sea interface in ways that depend on the temporal and spatial scales of the target fields. We will discuss several aspects of these types of coupled interactions including oceanic and atmospheric forecast applications. For oceanic mesoscale eddies, the coupling can influence the energetics of the oceanic flow itself. For Madden-Julian Oscillation onset, the coupling timestep should resolve the diurnal cycle to properly raise time-mean SST and latent heat flux prior to deep convection. For Atmospheric River events, the evolving SST field can alter the trajectory and intensity of precipitation anomalies along the California coast. Improvements in predictions will also rely on identifying and alleviating sources of biases in the climate states of the coupled system. Surprisingly, forecast skill can also be improved by enhancing stochastic variability in the atmospheric component of coupled models as found in a multiscale ensemble modeling approach.

An ensemble Kalman filter with a high-resolution atmosphere-ocean coupled model for tropical cyclone forecasts

NASA Astrophysics Data System (ADS)

Kunii, M.; Ito, K.; Wada, A.

2015-12-01

An ensemble Kalman filter (EnKF) using a regional mesoscale atmosphere-ocean coupled model was developed to represent the uncertainties of sea surface temperature (SST) in ensemble data assimilation strategies. The system was evaluated through data assimilation cycle experiments over a one-month period from July to August 2014, during which a tropical cyclone as well as severe rainfall events occurred. The results showed that the data assimilation cycle with the coupled model could reproduce SST distributions realistically even without updating SST and salinity during the data assimilation cycle. Therefore, atmospheric variables and radiation applied as a forcing to ocean models can control oceanic variables to some extent in the current data assimilation configuration. However, investigations of the forecast error covariance estimated in EnKF revealed that the correlation between atmospheric and oceanic variables could possibly lead to less flow-dependent error covariance for atmospheric variables owing to the difference in the time scales between atmospheric and oceanic variables. A verification of the analyses showed positive impacts of applying the ocean model to EnKF on precipitation forecasts. The use of EnKF with the coupled model system captured intensity changes of a tropical cyclone better than it did with an uncoupled atmosphere model, even though the impact on the track forecast was negligibly small.

Obtaining high-resolution stage forecasts by coupling large-scale hydrologic models with sensor data

NASA Astrophysics Data System (ADS)

Fries, K. J.; Kerkez, B.

2017-12-01

We investigate how "big" quantities of distributed sensor data can be coupled with a large-scale hydrologic model, in particular the National Water Model (NWM), to obtain hyper-resolution forecasts. The recent launch of the NWM provides a great example of how growing computational capacity is enabling a new generation of massive hydrologic models. While the NWM spans an unprecedented spatial extent, there remain many questions about how to improve forecast at the street-level, the resolution at which many stakeholders make critical decisions. Further, the NWM runs on supercomputers, so water managers who may have access to their own high-resolution measurements may not readily be able to assimilate them into the model. To that end, we ask the question: how can the advances of the large-scale NWM be coupled with new local observations to enable hyper-resolution hydrologic forecasts? A methodology is proposed whereby the flow forecasts of the NWM are directly mapped to high-resolution stream levels using Dynamical System Identification. We apply the methodology across a sensor network of 182 gages in Iowa. Of these sites, approximately one third have shown to perform well in high-resolution flood forecasting when coupled with the outputs of the NWM. The quality of these forecasts is characterized using Principal Component Analysis and Random Forests to identify where the NWM may benefit from new sources of local observations. We also discuss how this approach can help municipalities identify where they should place low-cost sensors to most benefit from flood forecasts of the NWM.

Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2): atmosphere-land-ocean-sea ice coupled prediction system for operational seasonal forecasting

NASA Astrophysics Data System (ADS)

Takaya, Yuhei; Hirahara, Shoji; Yasuda, Tamaki; Matsueda, Satoko; Toyoda, Takahiro; Fujii, Yosuke; Sugimoto, Hiroyuki; Matsukawa, Chihiro; Ishikawa, Ichiro; Mori, Hirotoshi; Nagasawa, Ryoji; Kubo, Yutaro; Adachi, Noriyuki; Yamanaka, Goro; Kuragano, Tsurane; Shimpo, Akihiko; Maeda, Shuhei; Ose, Tomoaki

2018-02-01

This paper describes the Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2), which was put into operation in June 2015 for the purpose of performing seasonal predictions. JMA/MRI-CPS2 has various upgrades from its predecessor, JMA/MRI-CPS1, including improved resolution and physics in its atmospheric and oceanic components, introduction of an interactive sea-ice model and realistic initialization of its land component. Verification of extensive re-forecasts covering a 30-year period (1981-2010) demonstrates that JMA/MRI-CPS2 possesses improved seasonal predictive skills for both atmospheric and oceanic interannual variability as well as key coupled variability such as the El Niño-Southern Oscillation (ENSO). For ENSO prediction, the new system better represents the forecast uncertainty and transition/duration of ENSO phases. Our analysis suggests that the enhanced predictive skills are attributable to incremental improvements resulting from all of the changes, as is apparent in the beneficial effects of sea-ice coupling and land initialization on 2-m temperature predictions. JMA/MRI-CPS2 is capable of reasonably representing the seasonal cycle and secular trends of sea ice. The sea-ice coupling remarkably enhances the predictive capability for the Arctic 2-m temperature, indicating the importance of this factor, particularly for seasonal predictions in the Arctic region.

AN OPERATIONAL EVALUATION OF THE ETA - CMAQ AIR QUALITY FORECAST MODEL

EPA Science Inventory

The National Oceanic and Atmospheric Administration (NOAA), in partnership with the United States Environmental Protection Agency (EPA), are developing an operational, nationwide Air Quality Forecasting (AQF) system. An experimental phase of this program, which couples NOAA's Et...

Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations

NASA Technical Reports Server (NTRS)

Penny, Stephen G.; Akella, Santha; Buehner, Mark; Chevallier, Matthieu; Counillon, Francois; Draper, Clara; Frolov, Sergey; Fujii, Yosuke; Karspeck, Alicia; Kumar, Arun

2017-01-01

The purpose of this report is to identify fundamental issues for coupled data assimilation (CDA), such as gaps in science and limitations in forecasting systems, in order to provide guidance to the World Meteorological Organization (WMO) on how to facilitate more rapid progress internationally. Coupled Earth system modeling provides the opportunity to extend skillful atmospheric forecasts beyond the traditional two-week barrier by extracting skill from low-frequency state components such as the land, ocean, and sea ice. More generally, coupled models are needed to support seamless prediction systems that span timescales from weather, subseasonal to seasonal (S2S), multiyear, and decadal. Therefore, initialization methods are needed for coupled Earth system models, either applied to each individual component (called Weakly Coupled Data Assimilation - WCDA) or applied the coupled Earth system model as a whole (called Strongly Coupled Data Assimilation - SCDA). Using CDA, in which model forecasts and potentially the state estimation are performed jointly, each model domain benefits from observations in other domains either directly using error covariance information known at the time of the analysis (SCDA), or indirectly through flux interactions at the model boundaries (WCDA). Because the non-atmospheric domains are generally under-observed compared to the atmosphere, CDA provides a significant advantage over single-domain analyses. Next, we provide a synopsis of goals, challenges, and recommendations to advance CDA: Goals: (a) Extend predictive skill beyond the current capability of NWP (e.g. as demonstrated by improving forecast skill scores), (b) produce physically consistent initial conditions for coupled numerical prediction systems and reanalyses (including consistent fluxes at the domain interfaces), (c) make best use of existing observations by allowing observations from each domain to influence and improve the full earth system analysis, (d) develop a robust observation-based identification and understanding of mechanisms that determine the variability of weather and climate, (e) identify critical weaknesses in coupled models and the earth observing system, (f) generate full-field estimates of unobserved or sparsely observed variables, (g) improve the estimation of the external forcings causing changes to climate, (h) transition successes from idealized CDA experiments to real-world applications. Challenges: (a) Modeling at the interfaces between interacting components of coupled Earth system models may be inadequate for estimating uncertainty or error covariances between domains, (b) current data assimilation methods may be insufficient to simultaneously analyze domains containing multiple spatiotemporal scales of interest, (c) there is no standardization of observation data or their delivery systems across domains, (d) the size and complexity of many large-scale coupled Earth system models makes it is difficult to accurately represent uncertainty due to model parameters and coupling parameters, (e) model errors lead to local biases that can transfer between the different Earth system components and lead to coupled model biases and long-term model drift, (e) information propagation across model components with different spatiotemporal scales is extremely complicated, and must be improved in current coupled modeling frameworks, (h) there is insufficient knowledge on how to represent evolving errors in non-atmospheric model components (e.g. as sea ice, land and ocean) on the timescales of NWP.

Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

NASA Astrophysics Data System (ADS)

Bao, Hongjun; Zhao, Linna

2012-02-01

A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, and show that TIGGE ensemble forecast data are a promising tool for forecasting of flood inundation, comparable with that driven by raingauge observations.

A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System: Dual-Resolution Implementation and Testing Results

NASA Astrophysics Data System (ADS)

Pan, Yujie; Xue, Ming; Zhu, Kefeng; Wang, Mingjun

2018-05-01

A dual-resolution (DR) version of a regional ensemble Kalman filter (EnKF)-3D ensemble variational (3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution (HR) deterministic background forecast with lower-resolution (LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/˜13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation (GSI) 3D variational (3DVar) analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar. Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.

Toward Sub-seasonal to Seasonal Arctic Sea Ice Forecasting Using the Regional Arctic System Model (RASM)

NASA Astrophysics Data System (ADS)

Kamal, S.; Maslowski, W.; Roberts, A.; Osinski, R.; Cassano, J. J.; Seefeldt, M. W.

2017-12-01

The Regional Arctic system model has been developed and used to advance the current state of Arctic modeling and increase the skill of sea ice forecast. RASM is a fully coupled, limited-area model that includes the atmosphere, ocean, sea ice, land hydrology and runoff routing components and the flux coupler to exchange information among them. Boundary conditions are derived from NCEP Climate Forecasting System Reanalyses (CFSR) or Era Iterim (ERA-I) for hindcast simulations or from NCEP Coupled Forecast System Model version 2 (CFSv2) for seasonal forecasts. We have used RASM to produce sea ice forecasts for September 2016 and 2017, in contribution to the Sea Ice Outlook (SIO) of the Sea Ice Prediction Network (SIPN). Each year, we produced three SIOs for the September minimum, initialized on June 1, July 1 and August 1. In 2016, predictions used a simple linear regression model to correct for systematic biases and included the mean September sea ice extent, the daily minimum and the week of the minimum. In 2017, we produced a 12-member ensemble on June 1 and July 1, and 28-member ensemble August 1. The predictions of September 2017 included the pan-Arctic and regional Alaskan sea ice extent, daily and monthly mean pan-Arctic maps of sea ice probability, concentration and thickness. No bias correction was applied to the 2017 forecasts. Finally, we will also discuss future plans for RASM forecasts, which include increased resolution for model components, ecosystem predictions with marine biogeochemistry extensions (mBGC) to the ocean and sea ice components, and feasibility of optional boundary conditions using the Navy Global Environmental Model (NAVGEM).

Oceanic sources of predictability for MJO propagation across the Maritime Continent in a subset of S2S forecast models

NASA Astrophysics Data System (ADS)

DeMott, C. A.; Klingaman, N. P.

2017-12-01

Skillful prediction of the Madden-Julian oscillation (MJO) passage across the Maritime Continent (MC) has important implications for global forecasts of high-impact weather events, such as atmospheric rivers and heat waves. The North American teleconnection response to the MJO is strongest when MJO convection is located in the western Pacific Ocean, but many climate and forecast models are deficient in their simulation of MC-crossing MJO events. Compared to atmosphere-only general circulation models (AGCMs), MJO simulation skill generally improves with the addition of ocean feedbacks in coupled GCMs (CGCMs). Using observations, previous studies have noted that the degree of ocean coupling may vary considerably from one MJO event to the next. The coupling mechanisms may be linked to the presence of ocean Equatorial Rossby waves, the sign and amplitude of Equatorial surface currents, and the upper ocean temperature and salinity profiles. In this study, we assess the role of ocean feedbacks to MJO prediction skill using a subset of CGCMs participating in the Subseasonal-to-Seasonal (S2S) Project database. Oceanic observational and reanalysis datasets are used to characterize the upper ocean background state for observed MJO events that do and do not propagate beyond the MC. The ability of forecast models to capture the oceanic influence on the MJO is first assessed by quantifying SST forecast skill. Next, a set of previously developed air-sea interaction diagnostics is applied to model output to measure the role of SST perturbations on the forecast MJO. The "SST effect" in forecast MJO events is compared to that obtained from reanalysis data. Leveraging all ensemble members of a given forecast helps disentangle oceanic model biases from atmospheric model biases, both of which can influence the expression of ocean feedbacks in coupled forecast systems. Results of this study will help identify areas of needed model improvement for improved MJO forecasts.

Ensemble Downscaling of Winter Seasonal Forecasts: The MRED Project

NASA Astrophysics Data System (ADS)

Arritt, R. W.; Mred Team

2010-12-01

The Multi-Regional climate model Ensemble Downscaling (MRED) project is a multi-institutional project that is producing large ensembles of downscaled winter seasonal forecasts from coupled atmosphere-ocean seasonal prediction models. Eight regional climate models each are downscaling 15-member ensembles from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the new NASA seasonal forecast system based on the GEOS5 atmospheric model coupled with the MOM4 ocean model. This produces 240-member ensembles, i.e., 8 regional models x 15 global ensemble members x 2 global models, for each winter season (December-April) of 1982-2003. Results to date show that combined global-regional downscaled forecasts have greatest skill for seasonal precipitation anomalies during strong El Niño events such as 1982-83 and 1997-98. Ensemble means of area-averaged seasonal precipitation for the regional models generally track the corresponding results for the global model, though there is considerable inter-model variability amongst the regional models. For seasons and regions where area mean precipitation is accurately simulated the regional models bring added value by extracting greater spatial detail from the global forecasts, mainly due to better resolution of terrain in the regional models. Our results also emphasize that an ensemble approach is essential to realizing the added value from the combined global-regional modeling system.

A probabilistic drought forecasting framework: A combined dynamical and statistical approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Hongxiang; Moradkhani, Hamid; Zarekarizi, Mahkameh

In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initialmore » condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.« less

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of offline modeling errors

NASA Astrophysics Data System (ADS)

Marti, Alejandro; Folch, Arnau

2018-03-01

Volcanic ash modeling systems are used to simulate the atmospheric dispersion of volcanic ash and to generate forecasts that quantify the impacts from volcanic eruptions on infrastructures, air quality, aviation, and climate. The efficiency of response and mitigation actions is directly associated with the accuracy of the volcanic ash cloud detection and modeling systems. Operational forecasts build on offline coupled modeling systems in which meteorological variables are updated at the specified coupling intervals. Despite the concerns from other communities regarding the accuracy of this strategy, the quantification of the systematic errors and shortcomings associated with the offline modeling systems has received no attention. This paper employs the NMMB-MONARCH-ASH model to quantify these errors by employing different quantitative and categorical evaluation scores. The skills of the offline coupling strategy are compared against those from an online forecast considered to be the best estimate of the true outcome. Case studies are considered for a synthetic eruption with constant eruption source parameters and for two historical events, which suitably illustrate the severe aviation disruptive effects of European (2010 Eyjafjallajökull) and South American (2011 Cordón Caulle) volcanic eruptions. Evaluation scores indicate that systematic errors due to the offline modeling are of the same order of magnitude as those associated with the source term uncertainties. In particular, traditional offline forecasts employed in operational model setups can result in significant uncertainties, failing to reproduce, in the worst cases, up to 45-70 % of the ash cloud of an online forecast. These inconsistencies are anticipated to be even more relevant in scenarios in which the meteorological conditions change rapidly in time. The outcome of this paper encourages operational groups responsible for real-time advisories for aviation to consider employing computationally efficient online dispersal models.

Statistical and Hydrological evaluation of precipitation forecasts from IMD MME and ECMWF numerical weather forecasts for Indian River basins

NASA Astrophysics Data System (ADS)

Mohite, A. R.; Beria, H.; Behera, A. K.; Chatterjee, C.; Singh, R.

2016-12-01

Flood forecasting using hydrological models is an important and cost-effective non-structural flood management measure. For forecasting at short lead times, empirical models using real-time precipitation estimates have proven to be reliable. However, their skill depreciates with increasing lead time. Coupling a hydrologic model with real-time rainfall forecasts issued from numerical weather prediction (NWP) systems could increase the lead time substantially. In this study, we compared 1-5 days precipitation forecasts from India Meteorological Department (IMD) Multi-Model Ensemble (MME) with European Center for Medium Weather forecast (ECMWF) NWP forecasts for over 86 major river basins in India. We then evaluated the hydrologic utility of these forecasts over Basantpur catchment (approx. 59,000 km2) of the Mahanadi River basin. Coupled MIKE 11 RR (NAM) and MIKE 11 hydrodynamic (HD) models were used for the development of flood forecast system (FFS). RR model was calibrated using IMD station rainfall data. Cross-sections extracted from SRTM 30 were used as input to the MIKE 11 HD model. IMD started issuing operational MME forecasts from the year 2008, and hence, both the statistical and hydrologic evaluation were carried out from 2008-2014. The performance of FFS was evaluated using both the NWP datasets separately for the year 2011, which was a large flood year in Mahanadi River basin. We will present figures and metrics for statistical (threshold based statistics, skill in terms of correlation and bias) and hydrologic (Nash Sutcliffe efficiency, mean and peak error statistics) evaluation. The statistical evaluation will be at pan-India scale for all the major river basins and the hydrologic evaluation will be for the Basantpur catchment of the Mahanadi River basin.

WRF model for precipitation simulation and its application in real-time flood forecasting in the Jinshajiang River Basin, China

NASA Astrophysics Data System (ADS)

Zhou, Jianzhong; Zhang, Hairong; Zhang, Jianyun; Zeng, Xiaofan; Ye, Lei; Liu, Yi; Tayyab, Muhammad; Chen, Yufan

2017-07-01

An accurate flood forecasting with long lead time can be of great value for flood prevention and utilization. This paper develops a one-way coupled hydro-meteorological modeling system consisting of the mesoscale numerical weather model Weather Research and Forecasting (WRF) model and the Chinese Xinanjiang hydrological model to extend flood forecasting lead time in the Jinshajiang River Basin, which is the largest hydropower base in China. Focusing on four typical precipitation events includes: first, the combinations and mode structures of parameterization schemes of WRF suitable for simulating precipitation in the Jinshajiang River Basin were investigated. Then, the Xinanjiang model was established after calibration and validation to make up the hydro-meteorological system. It was found that the selection of the cloud microphysics scheme and boundary layer scheme has a great impact on precipitation simulation, and only a proper combination of the two schemes could yield accurate simulation effects in the Jinshajiang River Basin and the hydro-meteorological system can provide instructive flood forecasts with long lead time. On the whole, the one-way coupled hydro-meteorological model could be used for precipitation simulation and flood prediction in the Jinshajiang River Basin because of its relatively high precision and long lead time.

Supporting Crop Loss Insurance Policy of Indonesia through Rice Yield Modelling and Forecasting

NASA Astrophysics Data System (ADS)

van Verseveld, Willem; Weerts, Albrecht; Trambauer, Patricia; de Vries, Sander; Conijn, Sjaak; van Valkengoed, Eric; Hoekman, Dirk; Grondard, Nicolas; Hengsdijk, Huib; Schrevel, Aart; Vlasbloem, Pieter; Klauser, Dominik

2017-04-01

The Government of Indonesia has decided on a crop insurance policy to assist Indonesia's farmers and to boost food security. To support the Indonesian government, the G4INDO project (www.g4indo.org) is developing/constructing an integrated platform implemented in the Delft-FEWS forecasting system (Werner et al., 2013). The integrated platform brings together remote sensed data (both visible and radar) and hydrologic, crop and reservoir modelling and forecasting to improve the modelling and forecasting of rice yield. The hydrological model (wflow_sbm), crop model (wflow_lintul) and reservoir models (RTC-Tools) are coupled on time stepping basis in the OpenStreams framework (see https://github.com/openstreams/wflow) and deployed in the integrated platform to support seasonal forecasting of water availability and crop yield. First we will show the general idea about the G4INDO project, the integrated platform (including Sentinel 1 & 2 data) followed by first (reforecast) results of the coupled models for predicting water availability and crop yield in the Brantas catchment in Java, Indonesia. Werner, M., Schellekens, J., Gijsbers, P., Van Dijk, M., Van den Akker, O. and Heynert K, 2013. The Delft-FEWS flow forecasting system, Environmental Modelling & Software; 40:65-77. DOI: 10.1016/j.envsoft.2012.07.010.

Applying a coupled hydrometeorological simulation system to flash flood forecasting over the Korean Peninsula

NASA Astrophysics Data System (ADS)

Ryu, Young; Lim, Yoon-Jin; Ji, Hee-Sook; Park, Hyun-Hee; Chang, Eun-Chul; Kim, Baek-Jo

2017-11-01

In flash flood forecasting, it is necessary to consider not only traditional meteorological variables such as precipitation, evapotranspiration, and soil moisture, but also hydrological components such as streamflow. To address this challenge, the application of high resolution coupled atmospheric-hydrological models is emerging as a promising alternative. This study demonstrates the feasibility of linking a coupled atmospheric-hydrological model (WRF/WRFHydro) with 150-m horizontal grid spacing for flash flood forecasting in Korea. The study area is the Namgang Dam basin in Southern Korea, a mountainous area located downstream of Jiri Mountain (1915 m in height). Under flash flood conditions, the simulated precipitation over the entire basin is comparable to the domain-averaged precipitation, but discharge data from WRF-Hydro shows some differences in the total available water and the temporal distribution of streamflow (given by the timing of the streamflow peak following precipitation), compared to observations. On the basis of sensitivity tests, the parameters controlling the infiltration of excess precipitation and channel roughness depending on stream order are refined and their influence on temporal distribution of streamflow is addressed with intent to apply WRF-Hydro to flash flood forecasting in the Namgang Dam basin. The simulation results from the WRF-Hydro model with optimized parameters demonstrate the potential utility of a coupled atmospheric-hydrological model for forecasting heavy rain-induced flash flooding over the Korean Peninsula.

Air-Sea Heat Flux Transfer for MJO Initiation Processes during DYNAMO/CINDY2011 in Extended-Range Forecasts

NASA Astrophysics Data System (ADS)

Hong, X.; Reynolds, C. A.; Doyle, J. D.

2016-12-01

In this study, two-sets of monthly forecasts for the period during the Dynamics of Madden-Julian Oscillation (MJO)/Cooperative Indian Ocean Experiment of Intraseasonal Variability (DAYNAMO/CINDY) in November 2011 are examined. Each set includes three forecasts with the first set from Navy Global Environmental Model (NAVGEM) and the second set from Navy's non-hydrostatic Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS®1). Three NAVGEM monthly forecasts have used sea surface temperature (SST) from persistent at the initial time, from Navy Coupled Ocean Data Assimilation (NCODA) analysis, and from coupled NAVGEM-Hybrid Coordinate Ocean Model (HYCOM) forecasts. Examination found that NAVGEM can predict the MJO at 20-days lead time using SST from analysis and from coupled NAVGEM-HYCOM but cannot predict the MJO using the persistent SST, in which a clear circumnavigating signal is absent. Three NAVGEM monthly forecasts are then applied as lateral boundary conditions for three COAMPS monthly forecasts. The results show that all COAMPS runs, including using lateral boundary conditions from the NAVGEM that is without the MJO signal, can predict the MJO. Vertically integrated moisture anomaly and 850-hPa wind anomaly in all COAMPS runs have indicated strong anomalous equatorial easterlies associated with Rossby wave prior to the MJO initiation. Strong surface heat fluxes and turbulence kinetic energy have promoted the convective instability and triggered anomalous ascending motion, which deepens moist boundary layer and develops deep convection into the upper troposphere to form the MJO phase. The results have suggested that air-sea interaction process is important for the initiation and development of the MJO. 1COAMPS® is a registered trademark of the Naval Research Laboratory

Mediterranea Forecasting System: a focus on wave-current coupling

NASA Astrophysics Data System (ADS)

Clementi, Emanuela; Delrosso, Damiano; Pistoia, Jenny; Drudi, Massimiliano; Fratianni, Claudia; Grandi, Alessandro; Pinardi, Nadia; Oddo, Paolo; Tonani, Marina

2016-04-01

The Mediterranean Forecasting System (MFS) is a numerical ocean prediction system that produces analyses, reanalyses and short term forecasts for the entire Mediterranean Sea and its Atlantic Ocean adjacent areas. MFS became operational in the late 90's and has been developed and continuously improved in the framework of a series of EU and National funded programs and is now part of the Copernicus Marine Service. The MFS is composed by the hydrodynamic model NEMO (Nucleus for European Modelling of the Ocean) 2-way coupled with the third generation wave model WW3 (WaveWatchIII) implemented in the Mediterranean Sea with 1/16 horizontal resolution and forced by ECMWF atmospheric fields. The model solutions are corrected by the data assimilation system (3D variational scheme adapted to the oceanic assimilation problem) with a daily assimilation cycle, using a background error correlation matrix varying seasonally and in different sub-regions of the Mediterranean Sea. The focus of this work is to present the latest modelling system upgrades and the related achieved improvements. In order to evaluate the performance of the coupled system a set of experiments has been built by coupling the wave and circulation models that hourly exchange the following fields: the sea surface currents and air-sea temperature difference are transferred from NEMO model to WW3 model modifying respectively the mean momentum transfer of waves and the wind speed stability parameter; while the neutral drag coefficient computed by WW3 model is passed to NEMO that computes the turbulent component. In order to validate the modelling system, numerical results have been compared with in-situ and remote sensing data. This work suggests that a coupled model might be capable of a better description of wave-current interactions, in particular feedback from the ocean to the waves might assess an improvement on the prediction capability of wave characteristics, while suggests to proceed toward a fully coupled modelling system in order to achieve stronger enhancements of the hydrodynamic fields.

The NRL relocatable ocean/acoustic ensemble forecast system

NASA Astrophysics Data System (ADS)

Rowley, C.; Martin, P.; Cummings, J.; Jacobs, G.; Coelho, E.; Bishop, C.; Hong, X.; Peggion, G.; Fabre, J.

2009-04-01

A globally relocatable regional ocean nowcast/forecast system has been developed to support rapid implementation of new regional forecast domains. The system is in operational use at the Naval Oceanographic Office for a growing number of regional and coastal implementations. The new system is the basis for an ocean acoustic ensemble forecast and adaptive sampling capability. We present an overview of the forecast system and the ocean ensemble and adaptive sampling methods. The forecast system consists of core ocean data analysis and forecast modules, software for domain configuration, surface and boundary condition forcing processing, and job control, and global databases for ocean climatology, bathymetry, tides, and river locations and transports. The analysis component is the Navy Coupled Ocean Data Assimilation (NCODA) system, a 3D multivariate optimum interpolation system that produces simultaneous analyses of temperature, salinity, geopotential, and vector velocity using remotely-sensed SST, SSH, and sea ice concentration, plus in situ observations of temperature, salinity, and currents from ships, buoys, XBTs, CTDs, profiling floats, and autonomous gliders. The forecast component is the Navy Coastal Ocean Model (NCOM). The system supports one-way nesting and multiple assimilation methods. The ensemble system uses the ensemble transform technique with error variance estimates from the NCODA analysis to represent initial condition error. Perturbed surface forcing or an atmospheric ensemble is used to represent errors in surface forcing. The ensemble transform Kalman filter is used to assess the impact of adaptive observations on future analysis and forecast uncertainty for both ocean and acoustic properties.

Progress and Challenges in Short to Medium Range Coupled Prediction

NASA Technical Reports Server (NTRS)

Brassington, G. B.; Martin, M. J.; Tolman, H. L.; Akella, Santha; Balmeseda, M.; Chambers, C. R. S.; Cummings, J. A.; Drillet, Y.; Jansen, P. A. E. M.; Laloyaux, P.;

Towards a National Hydrological Forecasting system for Canada : Lessons Learned from the Great Lakes and St. Lawrence Prediction System

NASA Astrophysics Data System (ADS)

Fortin, V.; Durnford, D.; Gaborit, E.; Davison, B.; Dimitrijevic, M.; Matte, P.

2016-12-01

Environment and Climate Change Canada has recently deployed a water cycle prediction system for the Great Lakes and St. Lawrence River. The model domain includes both the Canadian and US portions of the watershed. It provides 84-h forecasts of weather elements, lake level, lake ice cover and surface currents based on two-way coupling of the GEM numerical weather prediction (NWP) model with the NEMO ocean model. Streamflow of all the major tributaries of the Great Lakes and St. Lawrence River are estimated by the WATROUTE routing model, which routes the surface runoff forecasted by GEM's land-surface scheme and assimilates streamflow observations where available. Streamflow forecasts are updated twice daily and are disseminated through an OGC compliant web map service (WMS) and a web feature service (WFS). In this presentation, in addition to describing the system and documenting its forecast skill, we show how it is being used by clients for various environmental prediction applications. We then discuss the importance of two-way coupling, land-surface and hillslope modelling and the impact of horizontal resolution on hydrological prediction skill. In the second portion of the talk, we discuss plans for implementing a similar system at the national scale, using what we have learned in the Great Lakes and St. Lawrence watershed. Early results obtained for the headwaters of the Saskatchewan River as well as for the whole Nelson-Churchill watershed are presented.

An operational global ocean forecast system and its applications

NASA Astrophysics Data System (ADS)

Mehra, A.; Tolman, H. L.; Rivin, I.; Rajan, B.; Spindler, T.; Garraffo, Z. D.; Kim, H.

2012-12-01

A global Real-Time Ocean Forecast System (RTOFS) was implemented in operations at NCEP/NWS/NOAA on 10/25/2011. This system is based on an eddy resolving 1/12 degree global HYCOM (HYbrid Coordinates Ocean Model) and is part of a larger national backbone capability of ocean modeling at NWS in strong partnership with US Navy. The forecast system is run once a day and produces a 6 day long forecast using the daily initialization fields produced at NAVOCEANO using NCODA (Navy Coupled Ocean Data Assimilation), a 3D multi-variate data assimilation methodology. As configured within RTOFS, HYCOM has a horizontal equatorial resolution of 0.08 degrees or ~9 km. The HYCOM grid is on a Mercator projection from 78.64 S to 47 N and north of this it employs an Arctic dipole patch where the poles are shifted over land to avoid a singularity at the North Pole. This gives a mid-latitude (polar) horizontal resolution of approximately 7 km (3.5 km). The coastline is fixed at 10 m isobath with open Bering Straits. This version employs 32 hybrid vertical coordinate surfaces with potential density referenced to 2000 m. Vertical coordinates can be isopycnals, often best for resolving deep water masses, levels of equal pressure (fixed depths), best for the well mixed unstratified upper ocean and sigma-levels (terrain-following), often the best choice in shallow water. The dynamic ocean model is coupled to a thermodynamic energy loan ice model and uses a non-slab mixed layer formulation. The forecast system is forced with 3-hourly momentum, radiation and precipitation fluxes from the operational Global Forecast System (GFS) fields. Results include global sea surface height and three dimensional fields of temperature, salinity, density and velocity fields used for validation and evaluation against available observations. Several downstream applications of this forecast system will also be discussed which include search and rescue operations at US Coast Guard, navigation safety information provided by OPC using real time ocean model guidance from Global RTOFS surface ocean currents, operational guidance on radionuclide dispersion near Fukushima using 3D tracers, boundary conditions for various operational coastal ocean forecast systems (COFS) run by NOS etc.

Towards an integrated forecasting system for fisheries on habitat-bound stocks

NASA Astrophysics Data System (ADS)

Christensen, A.; Butenschön, M.; Gürkan, Z.; Allen, I. J.

2013-03-01

First results of a coupled modelling and forecasting system for fisheries on habitat-bound stocks are being presented. The system consists currently of three mathematically, fundamentally different model subsystems coupled offline: POLCOMS providing the physical environment implemented in the domain of the north-west European shelf, the SPAM model which describes sandeel stocks in the North Sea, and the third component, the SLAM model, which connects POLCOMS and SPAM by computing the physical-biological interaction. Our major experience by the coupling model subsystems is that well-defined and generic model interfaces are very important for a successful and extendable coupled model framework. The integrated approach, simulating ecosystem dynamics from physics to fish, allows for analysis of the pathways in the ecosystem to investigate the propagation of changes in the ocean climate and to quantify the impacts on the higher trophic level, in this case the sandeel population, demonstrated here on the basis of hindcast data. The coupled forecasting system is tested for some typical scientific questions appearing in spatial fish stock management and marine spatial planning, including determination of local and basin-scale maximum sustainable yield, stock connectivity and source/sink structure. Our presented simulations indicate that sandeel stocks are currently exploited close to the maximum sustainable yield, even though periodic overfishing seems to have occurred, but large uncertainty is associated with determining stock maximum sustainable yield due to stock inherent dynamics and climatic variability. Our statistical ensemble simulations indicates that the predictive horizon set by climate interannual variability is 2-6 yr, after which only an asymptotic probability distribution of stock properties, like biomass, are predictable.

Forecasting in the presence of expectations

NASA Astrophysics Data System (ADS)

Allen, R.; Zivin, J. G.; Shrader, J.

2016-05-01

Physical processes routinely influence economic outcomes, and actions by economic agents can, in turn, influence physical processes. This feedback creates challenges for forecasting and inference, creating the potential for complementarity between models from different academic disciplines. Using the example of prediction of water availability during a drought, we illustrate the potential biases in forecasts that only take part of a coupled system into account. In particular, we show that forecasts can alter the feedbacks between supply and demand, leading to inaccurate prediction about future states of the system. Although the example is specific to drought, the problem of feedback between expectations and forecast quality is not isolated to the particular model-it is relevant to areas as diverse as population assessments for conservation, balancing the electrical grid, and setting macroeconomic policy.

The Impact of Ocean Observations in Seasonal Climate Prediction

NASA Technical Reports Server (NTRS)

Rienecker, Michele; Keppenne, Christian; Kovach, Robin; Marshak, Jelena

2010-01-01

The ocean provides the most significant memory for the climate system. Hence, a critical element in climate forecasting with coupled models is the initialization of the ocean with states from an ocean data assimilation system. Remotely-sensed ocean surface fields (e.g., sea surface topography, SST, winds) are now available for extensive periods and have been used to constrain ocean models to provide a record of climate variations. Since the ocean is virtually opaque to electromagnetic radiation, the assimilation of these satellite data is essential to extracting the maximum information content. More recently, the Argo drifters have provided unprecedented sampling of the subsurface temperature and salinity. Although the duration of this observation set has been too short to provide solid statistical evidence of its impact, there are indications that Argo improves the forecast skill of coupled systems. This presentation will address the impact these different observations have had on seasonal climate predictions with the GMAO's coupled model.

Initial conditions and ENSO prediction using a coupled ocean-atmosphere model

NASA Astrophysics Data System (ADS)

Larow, T. E.; Krishnamurti, T. N.

1998-01-01

A coupled ocean-atmosphere initialization scheme using Newtonian relaxation has been developed for the Florida State University coupled ocean-atmosphere global general circulation model. The initialization scheme is used to initialize the coupled model for seasonal forecasting the boreal summers of 1987 and 1988. The atmosphere model is a modified version of the Florida State University global spectral model, resolution T-42. The ocean general circulation model consists of a slightly modified version of the Hamburg's climate group model described in Latif (1987) and Latif et al. (1993). The coupling is synchronous with information exchanged every two model hours. Using ECMWF atmospheric daily analysis and observed monthly mean SSTs, two, 1-year, time-dependent, Newtonian relaxation were performed using the coupled model prior to conducting the seasonal forecasts. The coupled initializations were conducted from 1 June 1986 to 1 June 1987 and from 1 June 1987 to 1 June 1988. Newtonian relaxation was applied to the prognostic atmospheric vorticity, divergence, temperature and dew point depression equations. In the ocean model the relaxation was applied to the surface temperature. Two, 10-member ensemble integrations were conducted to examine the impact of the coupled initialization on the seasonal forecasts. The initial conditions used for the ensembles are the ocean's final state after the initialization and the atmospheric initial conditions are ECMWF analysis. Examination of the SST root mean square error and anomaly correlations between observed and forecasted SSTs in the Niño-3 and Niño-4 regions for the 2 seasonal forecasts, show closer agreement between the initialized forecast than two, 10-member non-initialized ensemble forecasts. The main conclusion here is that a single forecast with the coupled initialization outperforms, in SST anomaly prediction, against each of the control forecasts (members of the ensemble) which do not include such an initialization, indicating possible importance for the inclusion of the atmosphere during the coupled initialization.

Implementation of Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Forecasts and their Performance Evaluations during 2008 over the Continental United States

EPA Science Inventory

The National Oceanic and Atmospheric Administration (NOAA), in partnership with the United States Environmental Protection Agency (EPA), is operationally implementing an Air Quality Forecast (AQF) system. This program, which couples NOAA's North American Mesoscale (NAM) weather p...

Impact of Ozone Radiative Feedbacks on Global Weather Forecasting

NASA Astrophysics Data System (ADS)

Ivanova, I.; de Grandpré, J.; Rochon, Y. J.; Sitwell, M.

2017-12-01

A coupled Chemical Data Assimilation system for ozone is being developed at Environment and Climate Change Canada (ECCC) with the goals to improve the forecasting of UV index and the forecasting of air quality with the Global Environmental Multi-scale (GEM) Model for Air quality and Chemistry (MACH). Furthermore, this system provides an opportunity to evaluate the benefit of ozone assimilation for improving weather forecasting with the ECCC Global Deterministic Prediction System (GDPS) for Numerical Weather Prediction (NWP). The present UV index forecasting system uses a statistical approach for evaluating the impact of ozone in clear-sky and cloudy conditions, and the use of real-time ozone analysis and ozone forecasts is highly desirable. Improving air quality forecasting with GEM-MACH further necessitates the development of integrated dynamical-chemical assimilation system. Upon its completion, real-time ozone analysis and ozone forecasts will also be available for piloting the regional air quality system, and for the computation of ozone heating rates, in replacement of the monthly mean ozone distribution currently used in the GDPS. Experiments with ozone radiative feedbacks were run with the GDPS at 25km resolution and 84 levels with a lid at 0.1 hPa and were initialized with ozone analysis that has assimilated total ozone column from OMI, OMPS, and GOME satellite instruments. The results show that the use of prognostic ozone for the computation of the heating/cooling rates has a significant impact on the temperature distribution throughout the stratosphere and upper troposphere regions. The impact of ozone assimilation is especially significant in the tropopause region, where ozone heating in the infrared wavelengths is important and ozone lifetime is relatively long. The implementation of the ozone radiative feedback in the GDPS requires addressing various issues related to model biases (temperature and humidity) and biases in equilibrium state (ozone mixing ratio, air temperature and overhead column ozone) used for the calculation of the linearized photochemical production and loss of ozone. Furthermore the radiative budget in the tropopause region is strongly affected by water vapor cooling, which impact requires further evaluation for the use in chemically coupled operational NWP systems.

Using Ice Predictions to Guide Submarines

DTIC Science & Technology

2016-01-01

the Arctic Cap Nowcast/ Forecast System (ACNFS) in September 2013. The ACNFS consists of a coupled ice -ocean model that assimilates available real...of the ice cover. The age of the sea ice serves as an indicator of its physical properties including surface roughness, melt pond coverage, and...the Arctic Cap Nowcast/Forecast System (ACNFS). Ice thickness is in meters for 11 September 2015. Thickness ranges from zero to five meters as shown

Evaluation of an operational water cycle prediction system for the Laurentian Great Lakes and St. Lawrence River

NASA Astrophysics Data System (ADS)

Fortin, Vincent; Durnford, Dorothy; Smith, Gregory; Dyck, Sarah; Martinez, Yosvany; Mackay, Murray; Winter, Barbara

2017-04-01

Environment and Climate Change Canada (ECCC) is implementing new numerical guidance products based on fully coupled numerical models to better inform the public as well as specialized users on the current and future state of various components of the water cycle, including stream flow and water levels. Outputs from this new system, named the Water Cycle Prediction System (WCPS), have been available for the Great Lakes and St. Lawrence River watershed since June 2016. WCPS links together ECCC's weather forecasting model, GEM, the 2-D ice model C-ICE, the 3-D lake and ocean model NEMO, and a 2-D hydrological model, WATROUTE. Information concerning the water cycle is passed between the models at intervals varying from a few minutes to one hour. It currently produces two forecasts per day for the next three days of the complete water cycle in the Great Lakes region, the largest freshwater lake system in the world. Products include spatially-varying precipitation, evaporation, river discharge, water level anomalies, surface water temperatures, ice coverage, and surface currents. These new products are of interest to water resources and management authority, flood forecasters, hydroelectricity producers, navigation, environmental disaster managers, search and rescue teams, agriculture, and the general public. This presentation focuses on the evaluation of various elements forecasted by the system, and weighs the advantages and disadvantages of running the system fully coupled.

Real-Time Ocean Prediction System for the East Coast of India

NASA Astrophysics Data System (ADS)

Warrior, H. V.

2016-02-01

The primary objective of the research work reported in this abstract was to develop a Realtime Environmental model for Ocean Dispersion and Impact (as part of an already in-place Decision Support System) for the purpose of radiological safety for the area along Kalpakkam (East Indian) coast. This system involves combining real-time ocean observations with numerical models of ocean processes to provide hindcasts, nowcasts and forecasts of currents, tides and waves. In this work we present the development of an Automated Coupled Atmospheric - Ocean Model (we call it IIT-CAOM) used to forecast the sea surface currents, sea surface temperature (SST) and salinity etc of the Bay of Bengal region under the influence of transient and unsteady atmospheric conditions. This method uses a coupling of Atmosphere and Ocean model. The models used here are the WRF for atmospheric simulations and POM for the ocean counterpart. It has a 3 km X 3 km resolution. This Coupled Model uses GFS (Global Forecast System) Data or FNL (Final Analyses) Data as initial conditions for jump-starting the atmospheric model. The Atmospheric model is run first thus extracting air temperature, wind speed and relative humidity. The heat flux subroutine computes the net heat flux, using above mentioned parameters data. The net heat flux feeds to the ocean model by simply adding net heat flux subroutine to the ocean model code without changing the model original structure. The online forecast of the IIT-CAOM is currently available in the web. The whole system has been automized and runs without any more manual support. The IIT-CAOM simulations have been carried out for Kalpakkam region, which is located on the East coast of India, about 70 km south of Chennai in Tamilnadu State and a three day forecast of sea surface currents, sea surface temperature (SST) and salinity, etc have been obtained.

Multi-model assessment of the impact of soil moisture initialization on mid-latitude summer predictability

NASA Astrophysics Data System (ADS)

Ardilouze, Constantin; Batté, L.; Bunzel, F.; Decremer, D.; Déqué, M.; Doblas-Reyes, F. J.; Douville, H.; Fereday, D.; Guemas, V.; MacLachlan, C.; Müller, W.; Prodhomme, C.

2017-12-01

Land surface initial conditions have been recognized as a potential source of predictability in sub-seasonal to seasonal forecast systems, at least for near-surface air temperature prediction over the mid-latitude continents. Yet, few studies have systematically explored such an influence over a sufficient hindcast period and in a multi-model framework to produce a robust quantitative assessment. Here, a dedicated set of twin experiments has been carried out with boreal summer retrospective forecasts over the 1992-2010 period performed by five different global coupled ocean-atmosphere models. The impact of a realistic versus climatological soil moisture initialization is assessed in two regions with high potential previously identified as hotspots of land-atmosphere coupling, namely the North American Great Plains and South-Eastern Europe. Over the latter region, temperature predictions show a significant improvement, especially over the Balkans. Forecast systems better simulate the warmest summers if they follow pronounced dry initial anomalies. It is hypothesized that models manage to capture a positive feedback between high temperature and low soil moisture content prone to dominate over other processes during the warmest summers in this region. Over the Great Plains, however, improving the soil moisture initialization does not lead to any robust gain of forecast quality for near-surface temperature. It is suggested that models biases prevent the forecast systems from making the most of the improved initial conditions.

An operational coupled wave-current forecasting system for the northern Adriatic Sea

NASA Astrophysics Data System (ADS)

Russo, A.; Coluccelli, A.; Deserti, M.; Valentini, A.; Benetazzo, A.; Carniel, S.

2012-04-01

Since 2005 an Adriatic implementation of the Regional Ocean Modeling System (AdriaROMS) is being producing operational short-term forecasts (72 hours) of some hydrodynamic properties (currents, sea level, temperature, salinity) of the Adriatic Sea at 2 km horizontal resolution and 20 vertical s-levels, on a daily basis. The main objective of AdriaROMS, which is managed by the Hydro-Meteo-Clima Service (SIMC) of ARPA Emilia Romagna, is to provide useful products for civil protection purposes (sea level forecasts, outputs to run other forecasting models as for saline wedge, oil spills and coastal erosion). In order to improve the forecasts in the coastal area, where most of the attention is focused, a higher resolution model (0.5 km, again with 20 vertical s-levels) has been implemented for the northern Adriatic domain. The new implementation is based on the Coupled-Ocean-Atmosphere-Wave-Sediment Transport Modeling System (COAWST)and adopts ROMS for the hydrodynamic and Simulating WAve Nearshore (SWAN) for the wave module, respectively. Air-sea fluxes are computed using forecasts produced by the COSMO-I7 operational atmospheric model. At the open boundary of the high resolution model, temperature, salinity and velocity fields are provided by AdriaROMS while the wave characteristics are provided by an operational SWAN implementation (also managed by SIMC). Main tidal components are imposed as well, derived from a tidal model. Work in progress is oriented now on the validation of model results by means of extensive comparisons with acquired hydrographic measurements (such as CTDs or XBTs from sea-truth campaigns), currents and waves acquired at observational sites (including those of SIMC, CNR-ISMAR network and its oceanographic tower, located off the Venice littoral) and satellite-derived wave-heights data. Preliminary results on the forecast waves denote how, especially during intense storms, the effect of coupling can lead to significant variations in the wave heights. Part of the activity has been funded by the EU FP VII program (project "MICORE", contract n. 202798) and by the Regione Veneto regional law 15/2007 (Progetto "MARINA").

Probabilistic versus deterministic skill in predicting the western North Pacific-East Asian summer monsoon variability with multimodel ensembles

NASA Astrophysics Data System (ADS)

Yang, Xiu-Qun; Yang, Dejian; Xie, Qian; Zhang, Yaocun; Ren, Xuejuan; Tang, Youmin

2017-04-01

Based on historical forecasts of three quasi-operational multi-model ensemble (MME) systems, this study assesses the superiority of coupled MME over contributing single-model ensembles (SMEs) and over uncoupled atmospheric MME in predicting the Western North Pacific-East Asian summer monsoon variability. The probabilistic and deterministic forecast skills are measured by Brier skill score (BSS) and anomaly correlation (AC), respectively. A forecast-format dependent MME superiority over SMEs is found. The probabilistic forecast skill of the MME is always significantly better than that of each SME, while the deterministic forecast skill of the MME can be lower than that of some SMEs. The MME superiority arises from both the model diversity and the ensemble size increase in the tropics, and primarily from the ensemble size increase in the subtropics. The BSS is composed of reliability and resolution, two attributes characterizing probabilistic forecast skill. The probabilistic skill increase of the MME is dominated by the dramatic improvement in reliability, while resolution is not always improved, similar to AC. A monotonic resolution-AC relationship is further found and qualitatively explained, whereas little relationship can be identified between reliability and AC. It is argued that the MME's success in improving the reliability arises from an effective reduction of the overconfidence in forecast distributions. Moreover, it is examined that the seasonal predictions with coupled MME are more skillful than those with the uncoupled atmospheric MME forced by persisting sea surface temperature (SST) anomalies, since the coupled MME has better predicted the SST anomaly evolution in three key regions.

Hyperspectral Remote Sensing of the Coastal Ocean: Adaptive Sampling and Forecasting of In situ Optical Properties

DTIC Science & Technology

2003-09-30

We are developing an integrated rapid environmental assessment capability that will be used to feed an ocean nowcast/forecast system. The goal is to develop a capacity for predicting the dynamics in inherent optical properties in coastal waters. This is being accomplished by developing an integrated observation system that is being coupled to a data assimilative hydrodynamic bio-optical ecosystem model. The system was used adaptively to calibrate hyperspectral remote sensing sensors in optically complex nearshore coastal waters.

Modelling and Forecasting of Rice Yield in support of Crop Insurance

NASA Astrophysics Data System (ADS)

Weerts, A.; van Verseveld, W.; Trambauer, P.; de Vries, S.; Conijn, S.; van Valkengoed, E.; Hoekman, D.; Hengsdijk, H.; Schrevel, A.

2016-12-01

The Government of Indonesia has embarked on a policy to bring crop insurance to all of Indonesia's farmers. To support the Indonesian government, the G4INDO project (www.g4indo.org) is developing/constructing an integrated platform for judging and handling insurance claims. The platform consists of bringing together remote sensed data (both visible and radar) and hydrologic and crop modelling and forecasting to improve predictions in one forecasting platform (i.e. Delft-FEWS, Werner et al., 2013). The hydrological model and crop model (LINTUL) are coupled on time stepping basis in the OpenStreams framework (see https://github.com/openstreams/wflow) and deployed in a Delft-FEWS forecasting platform to support seasonal forecasting of water availability and crop yield. First we will show the general idea about the project, the integrated platform (including Sentinel 1 & 2 data) followed by first (reforecast) results of the coupled models for predicting water availability and crop yield in the Brantas catchment in Java, Indonesia. Werner, M., Schellekens, J., Gijsbers, P., Van Dijk, M., Van den Akker, O. and Heynert K, 2013. The Delft-FEWS flow forecasting system, Environmental Modelling & Software; 40:65-77. DOI: 10.1016/j.envsoft.2012.07.010 .

Potential for malaria seasonal forecasting in Africa

NASA Astrophysics Data System (ADS)

Tompkins, Adrian; Di Giuseppe, Francesca; Colon-Gonzalez, Felipe; Namanya, Didas; Friday, Agabe

2014-05-01

As monthly and seasonal dynamical prediction systems have improved their skill in the tropics over recent years, there is now the potential to use these forecasts to drive dynamical malaria modelling systems to provide early warnings in epidemic and meso-endemic regions. We outline a new pilot operational system that has been developed at ECMWF and ICTP. It uses a precipitation bias correction methodology to seamlessly join the monthly ensemble prediction system (EPS) and seasonal (system 4) forecast systems of ECMWF together. The resulting temperature and rainfall forecasts for Africa are then used to drive the recently developed ICTP malaria model known as VECTRI. The resulting coupled system of ECMWF climate forecasts and VECTRI thus produces predictions of malaria prevalence rates and transmission intensity across Africa. The forecasts are filtered to highlight the regions and months in which the system has particular value due to high year to year variability. In addition to epidemic areas, these also include meso and hyper-endemic regions which undergo considerable variability in the onset months. We demonstrate the limits of the forecast skill as a function of lead-time, showing that for many areas the dynamical system can add one to two months additional warning time to a system based on environmental monitoring. We then evaluate the past forecasts against district level case data in Uganda and show that when interventions can be discounted, the system can show significant skill at predicting interannual variability in transmission intensity up to 3 or 4 months ahead at the district scale. The prospects for a operational implementation will be briefly discussed.

On operational flood forecasting system involving 1D/2D coupled hydraulic model and data assimilation

NASA Astrophysics Data System (ADS)

Barthélémy, S.; Ricci, S.; Morel, T.; Goutal, N.; Le Pape, E.; Zaoui, F.

2018-07-01

In the context of hydrodynamic modeling, the use of 2D models is adapted in areas where the flow is not mono-dimensional (confluence zones, flood plains). Nonetheless the lack of field data and the computational cost constraints limit the extensive use of 2D models for operational flood forecasting. Multi-dimensional coupling offers a solution with 1D models where the flow is mono-dimensional and with local 2D models where needed. This solution allows for the representation of complex processes in 2D models, while the simulated hydraulic state is significantly better than that of the full 1D model. In this study, coupling is implemented between three 1D sub-models and a local 2D model for a confluence on the Adour river (France). A Schwarz algorithm is implemented to guarantee the continuity of the variables at the 1D/2D interfaces while in situ observations are assimilated in the 1D sub-models to improve results and forecasts in operational mode as carried out by the French flood forecasting services. An implementation of the coupling and data assimilation (DA) solution with domain decomposition and task/data parallelism is proposed so that it is compatible with operational constraints. The coupling with the 2D model improves the simulated hydraulic state compared to a global 1D model, and DA improves results in 1D and 2D areas.

UK Environmental Prediction - integration and evaluation at the convective scale

NASA Astrophysics Data System (ADS)

Fallmann, Joachim; Lewis, Huw; Castillo, Juan Manuel; Pearson, David; Harris, Chris; Saulter, Andy; Bricheno, Lucy; Blyth, Eleanor

2016-04-01

It has long been understood that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. For example, high impact weather is typically manifested through various interactions and feedbacks between different components of the Earth System. Damaging high winds can lead to significant damage from the large waves and storm surge along coastlines. The impact of intense rainfall can be translated through saturated soils and land surface processes, high river flows and flooding inland. The substantial impacts on individuals, businesses and infrastructure of such events indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, NERC Centre for Ecology & Hydrology and NERC National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus has been on a 2-year Prototype project to demonstrate the UK coupled prediction concept in research mode. This presentation will provide an update on UK environmental prediction activities. We will present the results from the initial implementation of an atmosphere-land-ocean coupled system and discuss progress and initial results from further development to integrate wave interactions. We will discuss future directions and opportunities for collaboration in environmental prediction, and the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

GODAE Systems in Operation

DTIC Science & Technology

2009-10-09

Ocean Data Assimilation Scientist, Met Office, Exeter, UK. Shan Mei is Research Scientist, National Marine Environment Forecast Center, Beijing ...An MFS-MEDSLICK coupled system is operationally used for oil spill fore- casting in support of Regional Marine Pollution Emergency Response Centre...configura- tion with 11-km to 16-km horizontal resolution and 22 hybrid vertical layers. HYCOM is coupled to an Elastic Viscous Plastic dynamic and

Global Modeling and Assimilation Office Annual Report and Research Highlights 2011-2012

NASA Technical Reports Server (NTRS)

Rienecker, Michele M.

2012-01-01

Over the last year, the Global Modeling and Assimilation Office (GMAO) has continued to advance our GEOS-5-based systems, updating products for both weather and climate applications. We contributed hindcasts and forecasts to the National Multi-Model Ensemble (NMME) of seasonal forecasts and the suite of decadal predictions to the Coupled Model Intercomparison Project (CMIP5).

Seasonal Forecast Skill And Teleconnections Over East Africa

NASA Astrophysics Data System (ADS)

MacLeod, D.; Palmer, T.

2017-12-01

Many people living in East Africa are significantly exposed to risks arising from climate variability. The region experiences two rainy seasons and poor performance of either or both of these (such as seen recently in 2016/17) reduces agricultural productivity and threatens food security. In combination with other factors this can lead to famine. By utilizing seasonal climate forecasts, preparatory actions can be taken in order to mitigate the risks arising from such climate variability. As part of the project ForPAc: "Towards forecast-based preparedness action", we are working with humanitarian agencies in Kenya to build such early warning systems on subseasonal-to-seasonal timescales. Here, the seasonal predictability and forecast skill of the two East African rainy seasons will be presented. Results from the new ECMWF operational forecasting system SEAS5 will be shown and compared to the previous System 4. Analysis of a new 110 year long atmosphere-only simulation will also be discussed, demonstrating impacts of atmosphere-ocean coupling as well as putting operational forecast skill in a long-term context. Particular focus will be given to the model representation of teleconnections of seasonal climate with global sea surface temperatures; highlighting sources of forecast error and informing future model development.

Increasing vertical resolution in US models to improve track forecasts of Hurricane Joaquin with HWRF as an example

PubMed Central

Zhang, Banglin; Tallapragada, Vijay; Weng, Fuzhong; Liu, Qingfu; Sippel, Jason A.; Ma, Zaizhong; Bender, Morris A.

2016-01-01

The atmosphere−ocean coupled Hurricane Weather Research and Forecast model (HWRF) developed at the National Centers for Environmental Prediction (NCEP) is used as an example to illustrate the impact of model vertical resolution on track forecasts of tropical cyclones. A number of HWRF forecasting experiments were carried out at different vertical resolutions for Hurricane Joaquin, which occurred from September 27 to October 8, 2015, in the Atlantic Basin. The results show that the track prediction for Hurricane Joaquin is much more accurate with higher vertical resolution. The positive impacts of higher vertical resolution on hurricane track forecasts suggest that National Oceanic and Atmospheric Administration/NCEP should upgrade both HWRF and the Global Forecast System to have more vertical levels. PMID:27698121

Preliminary Study on Coupling Wave-Tide-Storm Surges Prediction System

NASA Astrophysics Data System (ADS)

You, S.; Park, S.; Seo, J.; Kim, K.

2008-12-01

The Korean Peninsula is surrounded by the Yellow Sea, East China Sea, and East Sea. This complex oceanographic system includes large tides in the Yellow Sea and seasonally varying monsoon and typhoon events. For Korea's coastal regions, floods caused by wave and storm surges are among the most serious threats. To predict more accurate wave and storm surge, the development of coupling wave-tide-storm surges prediction system is essential. For the time being, wave and storm surges predictions are still made separately in KMA (Korea Meteorological Administration) and most operational institute. However, many researchers have emphasized the effects of tides and storm surges on wind waves and recommended further investigations into the effects of wave-tide-storm surges interactions and coupling module on wave heights. However, tidal height and current give a great effect on the wave prediction in the Yellow sea where is very high tide and related research is not enough. At present, KMA has operated the wave (RWAM : Regional Wave Model) and storm surges/tide prediction system (RTSM : Regional Tide/Storm Surges Model) for ocean forecasting. The RWAM is WAVEWATCH III which is a third generation wave model developed by Tolman (1989). The RTSM is based on POM (Princeton Ocean Model, Blumberg and Mellor, 1987). The RWAM and RTSM cover the northwestern Pacific Ocean from 115°E to 150°E and from 20°N to 52°N. The horizontal grid intervals are 1/12° in both latitudinal and longitudinal directions. The development, testing and application of a coupling module in which wave-tide-storm surges are incorporated within the frame of KMA Ocean prediction system, has been considered as a step forward in respect of ocean forecasting. In addition, advanced wave prediction model will be applicable to the effect of ocean in the weather forecasting system. The main purpose of this study is to show how the coupling module developed and to report on a series of experiments dealing with the sensitivities and real case prediction of coupling wave-tide-storm surges prediction system.

UK Environmental Prediction - integration and evaluation at the convective scale

NASA Astrophysics Data System (ADS)

Fallmann, Joachim; Lewis, Huw; Castillo, Juan Manuel; Pearson, David; Harris, Chris; Saulter, Andy; Bricheno, Lucy; Blyth, Eleanor

2016-04-01

Traditionally, the simulation of regional ocean, wave and atmosphere components of the Earth System have been considered separately, with some information on other components provided by means of boundary or forcing conditions. More recently, the potential value of a more integrated approach, as required for global climate and Earth System prediction, for regional short-term applications has begun to gain increasing research effort. In the UK, this activity is motivated by an understanding that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. The substantial impacts on individuals, businesses and infrastructure of such events indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, NERC Centre for Ecology & Hydrology and NERC National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus has been on a 2-year Prototype project to demonstrate the UK coupled prediction concept in research mode. This presentation will provide an update on UK environmental prediction activities. We will present the results from the initial implementation of an atmosphere-land-ocean coupled system, including a new eddy-permitting resolution ocean component, and discuss progress and initial results from further development to integrate wave interactions in this relatively high resolution system. We will discuss future directions and opportunities for collaboration in environmental prediction, and the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

Online coupled regional meteorology-chemistry models in Europe: current status and prospects

NASA Astrophysics Data System (ADS)

Baklanov, A.; Schluenzen, K. H.; Suppan, P.; Baldasano, J.; Brunner, D.; Aksoyoglu, S.; Carmichael, G.; Douros, J.; Flemming, J.; Forkel, R.; Galmarini, S.; Gauss, M.; Grell, G.; Hirtl, M.; Joffre, S.; Jorba, O.; Kaas, E.; Kaasik, M.; Kallos, G.; Kong, X.; Korsholm, U.; Kurganskiy, A.; Kushta, J.; Lohmann, U.; Mahura, A.; Manders-Groot, A.; Maurizi, A.; Moussiopoulos, N.; Rao, S. T.; Savage, N.; Seigneur, C.; Sokhi, R.; Solazzo, E.; Solomos, S.; Sørensen, B.; Tsegas, G.; Vignati, E.; Vogel, B.; Zhang, Y.

2013-05-01

The simulation of the coupled evolution of atmospheric dynamics, pollutant transport, chemical reactions and atmospheric composition is one of the most challenging tasks in environmental modelling, climate change studies, and weather forecasting for the next decades as they all involve strongly integrated processes. Weather strongly influences air quality (AQ) and atmospheric transport of hazardous materials, while atmospheric composition can influence both weather and climate by directly modifying the atmospheric radiation budget or indirectly affecting cloud formation. Until recently, however, due to the scientific complexities and lack of computational power, atmospheric chemistry and weather forecasting have developed as separate disciplines, leading to the development of separate modelling systems that are only loosely coupled. The continuous increase in computer power has now reached a stage that enables us to perform online coupling of regional meteorological models with atmospheric chemical transport models. The focus on integrated systems is timely, since recent research has shown that meteorology and chemistry feedbacks are important in the context of many research areas and applications, including numerical weather prediction (NWP), AQ forecasting as well as climate and Earth system modelling. However, the relative importance of online integration and its priorities, requirements and levels of detail necessary for representing different processes and feedbacks can greatly vary for these related communities: (i) NWP, (ii) AQ forecasting and assessments, (iii) climate and earth system modelling. Additional applications are likely to benefit from online modelling, e.g.: simulation of volcanic ash or forest fire plumes, pollen warnings, dust storms, oil/gas fires, geo-engineering tests involving changes in the radiation balance. The COST Action ES1004 - European framework for online integrated air quality and meteorology modelling (EuMetChem) - aims at paving the way towards a new generation of online integrated atmospheric chemical transport and meteorology modelling with two-way interactions between different atmospheric processes including dynamics, chemistry, clouds, radiation, boundary layer and emissions. As its first task, we summarise the current status of European modelling practices and experience with online coupled modelling of meteorology with atmospheric chemistry including feedback mechanisms and attempt reviewing the various issues connected to the different modules of such online coupled models but also providing recommendations for coping with them for the benefit of the modelling community at large.

Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R. W.

2008-12-01

The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Can regional climate models provide additional useful information from global seasonal forecasts? MRED will use a suite of regional climate models to downscale seasonal forecasts produced by the new National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus will be on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the potential usefulness of higher resolution, especially for near-surface fields influenced by high resolution orography. Each regional model will cover the conterminous US (CONUS) at approximately 32 km resolution, and will perform an ensemble of 15 runs for each year 1982-2003 for the forecast period 1 December - 30 April. MRED will compare individual regional and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs), as well as wind, humidity, radiation, turbulent heat fluxes, which are important for more advanced coupled macro-scale hydrologic models. Metrics of ensemble spread will also be evaluated. Extensive analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will eventually define a strategy for more skillful and useful regional seasonal climate forecasts.

Monitoring the performance of the next Climate Forecast System version 3, throughout its development stage at EMC/NCEP

NASA Astrophysics Data System (ADS)

Peña, M.; Saha, S.; Wu, X.; Wang, J.; Tripp, P.; Moorthi, S.; Bhattacharjee, P.

2016-12-01

The next version of the operational Climate Forecast System (version 3, CFSv3) will be a fully coupled six-components system with diverse applications to earth system modeling, including weather and climate predictions. This system will couple the earth's atmosphere, land, ocean, sea-ice, waves and aerosols for both data assimilation and modeling. It will also use the NOAA Environmental Modeling System (NEMS) software super structure to couple these components. The CFSv3 is part of the next Unified Global Coupled System (UGCS), which will unify the global prediction systems that are now operational at NCEP. The UGCS is being developed through the efforts of dedicated research and engineering teams and through coordination across many CPO/MAPP and NGGPS groups. During this development phase, the UGCS is being tested for seasonal purposes and undergoes frequent revisions. Each new revision is evaluated to quickly discover, isolate and solve problems that negatively impact its performance. In the UGCS-seasonal model, components (e.g., ocean, sea-ice, atmosphere, etc.) are coupled through a NEMS-based "mediator". In this numerical infrastructure, model diagnostics and forecast validation are carried out, both component by component, and as a whole. The next stage, model optimization, will require enhanced performance diagnostics tools to help prioritize areas of numerical improvements. After the technical development of the UGCS-seasonal is completed, it will become the first realization of the CFSv3. All future development of this system will be carried out by the climate team at NCEP, in scientific collaboration with the groups that developed the individual components, as well as the climate community. A unique challenge to evaluate this unified weather-climate system is the large number of variables, which evolve over a wide range of temporal and spatial scales. A small set of performance measures and scorecard displays are been created, and collaboration and software contributions from research and operational centers are being incorporated. A status of the CFSv3/UGCS-seasonal development and examples of its performance and measuring tools will be presented.

The Rise of Complexity in Flood Forecasting: Opportunities, Challenges and Tradeoffs

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, M. P.; Nijssen, B.

2017-12-01

Operational flood forecasting is currently undergoing a major transformation. Most national flood forecasting services have relied for decades on lumped, highly calibrated conceptual hydrological models running on local office computing resources, providing deterministic streamflow predictions at gauged river locations that are important to stakeholders and emergency managers. A variety of recent technological advances now make it possible to run complex, high-to-hyper-resolution models for operational hydrologic prediction over large domains, and the US National Weather Service is now attempting to use hyper-resolution models to create new forecast services and products. Yet other `increased-complexity' forecasting strategies also exist that pursue different tradeoffs between model complexity (i.e., spatial resolution, physics) and streamflow forecast system objectives. There is currently a pressing need for a greater understanding in the hydrology community of the opportunities, challenges and tradeoffs associated with these different forecasting approaches, and for a greater participation by the hydrology community in evaluating, guiding and implementing these approaches. Intermediate-resolution forecast systems, for instance, use distributed land surface model (LSM) physics but retain the agility to deploy ensemble methods (including hydrologic data assimilation and hindcast-based post-processing). Fully coupled numerical weather prediction (NWP) systems, another example, use still coarser LSMs to produce ensemble streamflow predictions either at the model scale or after sub-grid scale runoff routing. Based on the direct experience of the authors and colleagues in research and operational forecasting, this presentation describes examples of different streamflow forecast paradigms, from the traditional to the recent hyper-resolution, to illustrate the range of choices facing forecast system developers. We also discuss the degree to which the strengths and weaknesses of each strategy map onto the requirements for different types of forecasting services (e.g., flash flooding, river flooding, seasonal water supply prediction).

Using Haines Index coupled with fire weather model predicted from high resolution LAM forecasts to asses wildfire extreme behaviour in Southern Europe.

NASA Astrophysics Data System (ADS)

Gaetani, Francesco; Baptiste Filippi, Jean; Simeoni, Albert; D'Andrea, Mirko

2010-05-01

Haines Index (HI) was developed by USDA Forest Service to measure the atmosphere's contribution to the growth potential of a wildfire. The Haines Index combines two atmospheric factors that are known to have an effect on wildfires: Stability and Dryness. As operational tools, HI proved its ability to predict plume dominated high intensity wildfires. However, since HI does not take into account the fuel continuity, composition and moisture conditions and the effects of wind and topography on fire behaviour, its use as forecasting tool should be carefully considered. In this work we propose the use of HI, predicted from HR Limited Area Model forecasts, coupled with a Fire Weather model (i.e., RISICO system) fully operational in Italy since 2003. RISICO is based on dynamic models able to represent in space and in time the effects that environment and vegetal physiology have on fuels and, in turn, on the potential behaviour of wildfires. The system automatically acquires from remote databases a thorough data-set of input information both of in situ and spatial nature. Meteorological observations, radar data, Limited Area Model weather forecasts, EO data, and fuel data are managed by a Unified Interface able to process a wide set of different data. Specific semi-physical models are used in the system to simulate the dynamics of the fuels (load and moisture contents of dead and live fuel) and the potential fire behaviour (rate of spread and linear intensity). A preliminary validation of this approach will be provided with reference to Sardinia and Corsica Islands, two major islands of the Mediterranean See frequently affected by extreme plume dominated wildfires. A time series of about 3000 wildfires burnt in Sardinia and Corsica in 2007 and 2008 will be used to evaluate the capability of HI coupled with the outputs of the Fire Weather model to forecast the actual risk in time and in space.

Testing a Coupled Global-limited-area Data Assimilation System using Observations from the 2004 Pacific Typhoon Season

NASA Astrophysics Data System (ADS)

Holt, C. R.; Szunyogh, I.; Gyarmati, G.; Hoffman, R. N.; Leidner, M.

2011-12-01

Tropical cyclone (TC) track and intensity forecasts have improved in recent years due to increased model resolution, improved data assimilation, and the rapid increase in the number of routinely assimilated observations over oceans. The data assimilation approach that has received the most attention in recent years is Ensemble Kalman Filtering (EnKF). The most attractive feature of the EnKF is that it uses a fully flow-dependent estimate of the error statistics, which can have important benefits for the analysis of rapidly developing TCs. We implement the Local Ensemble Transform Kalman Filter algorithm, a vari- ation of the EnKF, on a reduced-resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model and the NCEP Regional Spectral Model (RSM) to build a coupled global-limited area anal- ysis/forecast system. This is the first time, to our knowledge, that such a system is used for the analysis and forecast of tropical cyclones. We use data from summer 2004 to study eight tropical cyclones in the Northwest Pacific. The benchmark data sets that we use to assess the performance of our system are the NCEP Reanalysis and the NCEP Operational GFS analyses from 2004. These benchmark analyses were both obtained by the Statistical Spectral Interpolation, which was the operational data assimilation system of NCEP in 2004. The GFS Operational analysis assimilated a large number of satellite radiance observations in addition to the observations assimilated in our system. All analyses are verified against the Joint Typhoon Warning Center Best Track data set. The errors are calculated for the position and intensity of the TCs. The global component of the ensemble-based system shows improvement in po- sition analysis over the NCEP Reanalysis, but shows no significant difference from the NCEP operational analysis for most of the storm tracks. The regional com- ponent of our system improves position analysis over all the global analyses. The intensity analyses, measured by the minimum sea level pressure, are of similar quality in all of the analyses. Regional deterministic forecasts started from our analyses are generally not significantly different from those started from the GFS operational analysis. On average, the regional experiments performed better for longer than 48 h sea level pressure forecasts, while the global forecast performed better in predicting the position for longer than 48 h.

Assimilation of water temperature and discharge data for ensemble water temperature forecasting

NASA Astrophysics Data System (ADS)

Ouellet-Proulx, Sébastien; Chimi Chiadjeu, Olivier; Boucher, Marie-Amélie; St-Hilaire, André

2017-11-01

Recent work demonstrated the value of water temperature forecasts to improve water resources allocation and highlighted the importance of quantifying their uncertainty adequately. In this study, we perform a multisite cascading ensemble assimilation of discharge and water temperature on the Nechako River (Canada) using particle filters. Hydrological and thermal initial conditions were provided to a rainfall-runoff model, coupled to a thermal module, using ensemble meteorological forecasts as inputs to produce 5 day ensemble thermal forecasts. Results show good performances of the particle filters with improvements of the accuracy of initial conditions by more than 65% compared to simulations without data assimilation for both the hydrological and the thermal component. All thermal forecasts returned continuous ranked probability scores under 0.8 °C when using a set of 40 initial conditions and meteorological forecasts comprising 20 members. A greater contribution of the initial conditions to the total uncertainty of the system for 1-dayforecasts is observed (mean ensemble spread = 1.1 °C) compared to meteorological forcings (mean ensemble spread = 0.6 °C). The inclusion of meteorological uncertainty is critical to maintain reliable forecasts and proper ensemble spread for lead times of 2 days and more. This work demonstrates the ability of the particle filters to properly update the initial conditions of a coupled hydrological and thermal model and offers insights regarding the contribution of two major sources of uncertainty to the overall uncertainty in thermal forecasts.

Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

NASA Astrophysics Data System (ADS)

Li, Ji; Chen, Yangbo; Wang, Huanyu; Qin, Jianming; Li, Jie; Chiao, Sen

2017-03-01

Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1-15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km  × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.

Performance of a coupled lagged ensemble weather and river runoff prediction model system for the Alpine Ammer River catchment

NASA Astrophysics Data System (ADS)

Smiatek, G.; Kunstmann, H.; Werhahn, J.

2012-04-01

The Ammer River catchment located in the Bavarian Ammergau Alps and alpine forelands, Germany, represents with elevations reaching 2185 m and annual mean precipitation between1100 and 2000 mm a very demanding test ground for a river runoff prediction system. Large flooding events in 1999 and 2005 motivated the development of a physically based prediction tool in this area. Such a tool is the coupled high resolution numerical weather and river runoff forecasting system AM-POE that is being studied in several configurations in various experiments starting from the year 2005. Corner stones of the coupled system are the hydrological water balance model WaSiM-ETH run at 100 m grid resolution, the numerical weather prediction model (NWP) MM5 driven at 3.5 km grid cell resolution and the Perl Object Environment (POE) framework. POE implements the input data download from various sources, the input data provision via SOAP based WEB services as well as the runs of the hydrology model both with observed and with NWP predicted meteorology input. The one way coupled system utilizes a lagged ensemble prediction system (EPS) taking into account combination of recent and previous NWP forecasts. Results obtained in the years 2005-2011 reveal that river runoff simulations depict high correlation with observed runoff when driven with monitored observations in hindcast experiments. The ability to runoff forecasts is depending on lead times in the lagged ensemble prediction and shows still limitations resulting from errors in timing and total amount of the predicted precipitation in the complex mountainous area. The presentation describes the system implementation, and demonstrates the application of the POE framework in networking, distributed computing and in the setup of various experiments as well as long term results of the system application in the years 2005 - 2011.

Initialization and Predictability of a Coupled ENSO Forecast Model

NASA Technical Reports Server (NTRS)

Chen, Dake; Zebiak, Stephen E.; Cane, Mark A.; Busalacchi, Antonio J.

1997-01-01

The skill of a coupled ocean-atmosphere model in predicting ENSO has recently been improved using a new initialization procedure in which initial conditions are obtained from the coupled model, nudged toward observations of wind stress. The previous procedure involved direct insertion of wind stress observations, ignoring model feedback from ocean to atmosphere. The success of the new scheme is attributed to its explicit consideration of ocean-atmosphere coupling and the associated reduction of "initialization shock" and random noise. The so-called spring predictability barrier is eliminated, suggesting that such a barrier is not intrinsic to the real climate system. Initial attempts to generalize the nudging procedure to include SST were not successful; possible explanations are offered. In all experiments forecast skill is found to be much higher for the 1980s than for the 1970s and 1990s, suggesting decadal variations in predictability.

Impact of atmosphere and land surface initial conditions on seasonal forecast of global surface temperature

NASA Astrophysics Data System (ADS)

Materia, Stefano; Borrelli, Andrea; Bellucci, Alessio; Alessandri, Andrea; Di Pietro, Pierluigi; Athanasiadis, Panagiotis; Navarra, Antonio; Gualdi, Silvio

2014-05-01

The impact of land surface and atmosphere initialization on the forecast skill of a seasonal prediction system is investigated, and an effort to disentangle the role played by the individual components to the global predictability is done, via a hierarchy of seasonal forecast experiments performed under different initialization strategies. A realistic atmospheric initial state allows an improved equilibrium between the ocean and overlying atmosphere, mitigating the coupling shock and possibly increasing the model predictive skill in the ocean. In fact, in a few regions characterized by strong air-sea coupling, the atmosphere initial condition affects the forecast skill for several months. In particular, the ENSO region, the eastern tropical Atlantic and the North Pacific benefit significantly from the atmosphere initialization. On mainland, the impact of atmospheric initial conditions is detected in the early phase of the forecast, while the quality of land surface initialization affects the forecast skill in the following lead seasons. The winter forecast in the high latitude plains of Siberia and Canada benefit from the snow initialization, while the impact of soil moisture initial state is particularly effective in the Mediterranean region, in central Asia and Australia. However, initialization through land surface reanalysis does not systematically guarantee an enhancement of the predictive skill: the quality of the forecast is sometimes higher for the non-constrained model. Overall, the introduction of a realistic initialization of land surface and atmosphere substantially increases skill and accuracy. However, further developments in the operating procedure for land surface initialization are required for more accurate seasonal forecasts.

Forecasting the ocean optical environment in support of Navy mine warfare operations

NASA Astrophysics Data System (ADS)

Ladner, S. D.; Arnone, R.; Jolliff, J.; Casey, B.; Matulewski, K.

2012-06-01

A 3D ocean optical forecast system called TODS (Tactical Ocean Data System) has been developed to determine the performance of underwater LIDAR detection/identification systems. TODS fuses optical measurements from gliders, surface satellite optical properties, and 3D ocean forecast circulation models to extend the 2-dimensional surface satellite optics into a 3-dimensional optical volume including subsurface optical layers of beam attenuation coefficient (c) and diver visibility. Optical 3D nowcast and forecasts are combined with electro-optical identification (EOID) models to determine the underwater LIDAR imaging performance field used to identify subsurface mine threats in rapidly changing coastal regions. TODS was validated during a recent mine warfare exercise with Helicopter Mine Countermeasures Squadron (HM-14). Results include the uncertainties in the optical forecast and lidar performance and sensor tow height predictions that are based on visual detection and identification metrics using actual mine target images from the EOID system. TODS is a new capability of coupling the 3D optical environment and EOID system performance and is proving important for the MIW community as both a tactical decision aid and for use in operational planning, improving timeliness and efficiency in clearance operations.

Forecasting wildland fire behavior using high-resolution large-eddy simulations

NASA Astrophysics Data System (ADS)

Munoz-Esparza, D.; Kosovic, B.; Jimenez, P. A.; Anderson, A.; DeCastro, A.; Brown, B.

2016-12-01

Wildland fires are responsible for large socio-economic impacts. Fires affect the environment, damage structures, threaten lives, cause health issues, and involve large suppression costs. These impacts can be mitigated via accurate fire spread forecast to inform the incident management team. To this end, the state of Colorado is funding the development of the Colorado Fire Prediction System (CO-FPS). The system is based on the Weather Research and Forecasting (WRF) model enhanced with a fire behavior module (WRF-Fire). Realistic representation of wildland fire behavior requires explicit representation of small scale weather phenomena to properly account for coupled atmosphere-wildfire interactions. Moreover, transport and dispersion of biomass burning emissions from wildfires is controlled by turbulent processes in the atmospheric boundary layer, which are difficult to parameterize and typically lead to large errors when simplified source estimation and injection height methods are used. Therefore, we utilize turbulence-resolving large-eddy simulations at a resolution of 111 m to forecast fire spread and smoke distribution using a coupled atmosphere-wildfire model. This presentation will describe our improvements to the level-set based fire-spread algorithm in WRF-Fire and an evaluation of the operational system using 12 wildfire events that occurred in Colorado in 2016, as well as other historical fires. In addition, the benefits of explicit representation of turbulence for smoke transport and dispersion will be demonstrated.

Forecasting wildland fire behavior using high-resolution large-eddy simulations

NASA Astrophysics Data System (ADS)

Munoz-Esparza, D.; Kosovic, B.; Jimenez, P. A.; Anderson, A.; DeCastro, A.; Brown, B.

2017-12-01

Wildland fires are responsible for large socio-economic impacts. Fires affect the environment, damage structures, threaten lives, cause health issues, and involve large suppression costs. These impacts can be mitigated via accurate fire spread forecast to inform the incident management team. To this end, the state of Colorado is funding the development of the Colorado Fire Prediction System (CO-FPS). The system is based on the Weather Research and Forecasting (WRF) model enhanced with a fire behavior module (WRF-Fire). Realistic representation of wildland fire behavior requires explicit representation of small scale weather phenomena to properly account for coupled atmosphere-wildfire interactions. Moreover, transport and dispersion of biomass burning emissions from wildfires is controlled by turbulent processes in the atmospheric boundary layer, which are difficult to parameterize and typically lead to large errors when simplified source estimation and injection height methods are used. Therefore, we utilize turbulence-resolving large-eddy simulations at a resolution of 111 m to forecast fire spread and smoke distribution using a coupled atmosphere-wildfire model. This presentation will describe our improvements to the level-set based fire-spread algorithm in WRF-Fire and an evaluation of the operational system using 12 wildfire events that occurred in Colorado in 2016, as well as other historical fires. In addition, the benefits of explicit representation of turbulence for smoke transport and dispersion will be demonstrated.

Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons

NASA Astrophysics Data System (ADS)

Misra, Vasubandhu; Li, H.; Wu, Z.; DiNapoli, S.

2014-03-01

This paper shows demonstrable improvement in the global seasonal climate predictability of boreal summer (at zero lead) and fall (at one season lead) seasonal mean precipitation and surface temperature from a two-tiered seasonal hindcast forced with forecasted SST relative to two other contemporary operational coupled ocean-atmosphere climate models. The results from an extensive set of seasonal hindcasts are analyzed to come to this conclusion. This improvement is attributed to: (1) The multi-model bias corrected SST used to force the atmospheric model. (2) The global atmospheric model which is run at a relatively high resolution of 50 km grid resolution compared to the two other coupled ocean-atmosphere models. (3) The physics of the atmospheric model, especially that related to the convective parameterization scheme. The results of the seasonal hindcast are analyzed for both deterministic and probabilistic skill. The probabilistic skill analysis shows that significant forecast skill can be harvested from these seasonal hindcasts relative to the deterministic skill analysis. The paper concludes that the coupled ocean-atmosphere seasonal hindcasts have reached a reasonable fidelity to exploit their SST anomaly forecasts to force such relatively higher resolution two tier prediction experiments to glean further boreal summer and fall seasonal prediction skill.

An assessment of Indian monsoon seasonal forecasts and mechanisms underlying monsoon interannual variability in the Met Office GloSea5-GC2 system

NASA Astrophysics Data System (ADS)

Johnson, Stephanie J.; Turner, Andrew; Woolnough, Steven; Martin, Gill; MacLachlan, Craig

2017-03-01

We assess Indian summer monsoon seasonal forecasts in GloSea5-GC2, the Met Office fully coupled subseasonal to seasonal ensemble forecasting system. Using several metrics, GloSea5-GC2 shows similar skill to other state-of-the-art seasonal forecast systems. The prediction skill of the large-scale South Asian monsoon circulation is higher than that of Indian monsoon rainfall. Using multiple linear regression analysis we evaluate relationships between Indian monsoon rainfall and five possible drivers of monsoon interannual variability. Over the time period studied (1992-2011), the El Niño-Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) are the most important of these drivers in both observations and GloSea5-GC2. Our analysis indicates that ENSO and its teleconnection with Indian rainfall are well represented in GloSea5-GC2. However, the relationship between the IOD and Indian rainfall anomalies is too weak in GloSea5-GC2, which may be limiting the prediction skill of the local monsoon circulation and Indian rainfall. We show that this weak relationship likely results from a coupled mean state bias that limits the impact of anomalous wind forcing on SST variability, resulting in erroneous IOD SST anomalies. Known difficulties in representing convective precipitation over India may also play a role. Since Indian rainfall responds weakly to the IOD, it responds more consistently to ENSO than in observations. Our assessment identifies specific coupled biases that are likely limiting GloSea5-GC2 Indian summer monsoon seasonal prediction skill, providing targets for model improvement.

Accuracy of short‐term sea ice drift forecasts using a coupled ice‐ocean model

PubMed Central

Zhang, Jinlun

2015-01-01

Abstract Arctic sea ice drift forecasts of 6 h–9 days for the summer of 2014 are generated using the Marginal Ice Zone Modeling and Assimilation System (MIZMAS); the model is driven by 6 h atmospheric forecasts from the Climate Forecast System (CFSv2). Forecast ice drift speed is compared to drifting buoys and other observational platforms. Forecast positions are compared with actual positions 24 h–8 days since forecast. Forecast results are further compared to those from the forecasts generated using an ice velocity climatology driven by multiyear integrations of the same model. The results are presented in the context of scheduling the acquisition of high‐resolution images that need to follow buoys or scientific research platforms. RMS errors for ice speed are on the order of 5 km/d for 24–48 h since forecast using the sea ice model compared with 9 km/d using climatology. Predicted buoy position RMS errors are 6.3 km for 24 h and 14 km for 72 h since forecast. Model biases in ice speed and direction can be reduced by adjusting the air drag coefficient and water turning angle, but the adjustments do not affect verification statistics. This suggests that improved atmospheric forecast forcing may further reduce the forecast errors. The model remains skillful for 8 days. Using the forecast model increases the probability of tracking a target drifting in sea ice with a 10 km × 10 km image from 60 to 95% for a 24 h forecast and from 27 to 73% for a 48 h forecast. PMID:27818852

A coupled human-natural system to assess the operational value of weather and climate services for agriculture

NASA Astrophysics Data System (ADS)

Li, Yu; Giuliani, Matteo; Castelletti, Andrea

2017-09-01

Recent advances in weather and climate (W&C) services are showing increasing forecast skills over seasonal and longer timescales, potentially providing valuable support in informing decisions in a variety of economic sectors. Quantifying this value, however, might not be straightforward as better forecast quality does not necessarily imply better decisions by the end users, especially when forecasts do not reach their final users, when providers are not trusted, or when forecasts are not appropriately understood. In this study, we contribute an assessment framework to evaluate the operational value of W&C services for informing agricultural practices by complementing traditional forecast quality assessments with a coupled human-natural system behavioural model which reproduces farmers' decisions. This allows a more critical assessment of the forecast value mediated by the end users' perspective, including farmers' risk attitudes and behavioural factors. The application to an agricultural area in northern Italy shows that the quality of state-of-the-art W&C services is still limited in predicting the weather and the crop yield of the incoming agricultural season, with ECMWF annual products simulated by the IFS/HOPE model resulting in the most skillful product in the study area. However, we also show that the accuracy of estimating crop yield and the probability of making optimal decisions are not necessarily linearly correlated, with the overall assessment procedure being strongly impacted by the behavioural attitudes of farmers, which can produce rank reversals in the quantification of the W&C services operational value depending on the different perceptions of risk and uncertainty.

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

PubMed Central

Jensen, Tue V.; Pinson, Pierre

2017-01-01

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation. PMID:29182600

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system.

PubMed

Jensen, Tue V; Pinson, Pierre

2017-11-28

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

NASA Astrophysics Data System (ADS)

Jensen, Tue V.; Pinson, Pierre

2017-11-01

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

An improved procedure for El Nino forecasting: Implications for predictability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, D.; Zebiak, S.E.; Cane, M.A.

A coupled ocean-atmosphere data assimilation procedure yields improved forecasts of El Nino for the 1980s compared with previous forecasting procedures. As in earlier forecasts with the same model, no oceanic data were used, and only wind information was assimilated. The improvement is attributed to the explicit consideration of air-sea interaction in the initialization. These results suggest that El Nino is more predictable than previously estimated, but that predictability may vary on decadal or longer time scales. This procedure also eliminates the well-known spring barrier to El Nino prediction, which implies that it may not be intrinsic to the real climatemore » system. 24 refs., 5 figs., 1 tab.« less

Hyperspectral Remote Sensing of the Coastal Ocean: Adaptive Sampling and Forecasting of In situ Optical Properties

DTIC Science & Technology

2002-09-30

integrated observation system that is being coupled to a data assimilative hydrodynamic bio-optical ecosystem model. The system was used adaptively to develop hyperspectral remote sensing techniques in optically complex nearshore coastal waters.

Supporting inland waterway transport on German waterways by operational forecasting services - water-levels, discharges, river ice

NASA Astrophysics Data System (ADS)

Meißner, Dennis; Klein, Bastian; Ionita, Monica; Hemri, Stephan; Rademacher, Silke

2017-04-01

Inland waterway transport (IWT) is an important commercial sector significantly vulnerable to hydrological impacts. River ice and floods limit the availability of the waterway network and may cause considerable damages to waterway infrastructure. Low flows significantly affect IWT's operation efficiency usually several months a year due to the close correlation of (low) water levels / water depths and (high) transport costs. Therefore "navigation-related" hydrological forecasts focussing on the specific requirements of water-bound transport (relevant forecast locations, target parameters, skill characteristics etc.) play a major role in order to mitigate IWT's vulnerability to hydro-meteorological impacts. In light of continuing transport growth within the European Union, hydrological forecasts for the waterways are essential to stimulate the use of the free capacity IWT still offers more consequently. An overview of the current operational and pre-operational forecasting systems for the German waterways predicting water levels, discharges and river ice thickness on various time-scales will be presented. While short-term (deterministic) forecasts have a long tradition in navigation-related forecasting, (probabilistic) forecasting services offering extended lead-times are not yet well-established and are still subject to current research and development activities (e.g. within the EU-projects EUPORIAS and IMPREX). The focus is on improving technical aspects as well as on exploring adequate ways of disseminating and communicating probabilistic forecast information. For the German stretch of the River Rhine, one of the most frequented inland waterways worldwide, the existing deterministic forecast scheme has been extended by ensemble forecasts combined with statistical post-processing modules applying EMOS (Ensemble Model Output Statistics) and ECC (Ensemble Copula Coupling) in order to generate water level predictions up to 10 days and to estimate its predictive uncertainty properly. Additionally for the key locations at the international waterways Rhine, Elbe and Danube three competing forecast approaches are currently tested in a pre-operational set-up in order to generate monthly to seasonal (up to 3 months) forecasts: (1) the well-known Ensemble Streamflow Prediction approach (ensemble based on historical meteorology), (2) coupling hydrological models with post-processed outputs from ECMWF's general circulation model (System 4), and (3) a purely statistical approach based on the stable relationship (teleconnection) of global or regional oceanic, climate and hydrological data with river flows. The current results, still pre-operational, reveal the existence of a valuable predictability of water levels and streamflow also at monthly up to seasonal time-scales along the larger rivers used as waterways in Germany. Last but not least insight into the technical set-up of the aforementioned forecasting systems operated at the Federal Institute of Hydrology, which are based on a Delft-FEWS application, will be given focussing on the step-wise extension of the former system by integrating new components in order to meet the growing needs of the customers and to improve and extend the forecast portfolio for waterway users.

Predicting financial market crashes using ghost singularities.

PubMed

Smug, Damian; Ashwin, Peter; Sornette, Didier

2018-01-01

We analyse the behaviour of a non-linear model of coupled stock and bond prices exhibiting periodically collapsing bubbles. By using the formalism of dynamical system theory, we explain what drives the bubbles and how foreshocks or aftershocks are generated. A dynamical phase space representation of that system coupled with standard multiplicative noise rationalises the log-periodic power law singularity pattern documented in many historical financial bubbles. The notion of 'ghosts of finite-time singularities' is introduced and used to estimate the end of an evolving bubble, using finite-time singularities of an approximate normal form near the bifurcation point. We test the forecasting skill of this method on different stochastic price realisations and compare with Monte Carlo simulations of the full system. Remarkably, the approximate normal form is significantly more precise and less biased. Moreover, the method of ghosts of singularities is less sensitive to the noise realisation, thus providing more robust forecasts.

Predicting financial market crashes using ghost singularities

PubMed Central

2018-01-01

We analyse the behaviour of a non-linear model of coupled stock and bond prices exhibiting periodically collapsing bubbles. By using the formalism of dynamical system theory, we explain what drives the bubbles and how foreshocks or aftershocks are generated. A dynamical phase space representation of that system coupled with standard multiplicative noise rationalises the log-periodic power law singularity pattern documented in many historical financial bubbles. The notion of ‘ghosts of finite-time singularities’ is introduced and used to estimate the end of an evolving bubble, using finite-time singularities of an approximate normal form near the bifurcation point. We test the forecasting skill of this method on different stochastic price realisations and compare with Monte Carlo simulations of the full system. Remarkably, the approximate normal form is significantly more precise and less biased. Moreover, the method of ghosts of singularities is less sensitive to the noise realisation, thus providing more robust forecasts. PMID:29596485

Real-time drought forecasting system for irrigation management

NASA Astrophysics Data System (ADS)

Ceppi, A.; Ravazzani, G.; Corbari, C.; Salerno, R.; Meucci, S.; Mancini, M.

2014-09-01

In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in European areas which traditionally have an abundant supply of water, such as the Po Valley in northern Italy. In dry periods, water shortage problems can be enhanced by conflicting uses of water, such as irrigation, industry and power production (hydroelectric and thermoelectric). Furthermore, in the last decade the social perspective in relation to this issue has been increasing due to the possible impact of climate change and global warming scenarios which emerge from the IPCC Fifth Assessment Report (IPCC, 2013). Hence, the increased frequency of drought periods has stimulated the improvement of irrigation and water management. In this study we show the development and implementation of the PREGI real-time drought forecasting system; PREGI is an Italian acronym that means "hydro-meteorological forecast for irrigation management". The system, planned as a tool for irrigation optimization, is based on meteorological ensemble forecasts (20 members) at medium range (30 days) coupled with hydrological simulations of water balance to forecast the soil water content on a maize field in the Muzza Bassa Lodigiana (MBL) consortium in northern Italy. The hydrological model was validated against measurements of latent heat flux acquired by an eddy-covariance station, and soil moisture measured by TDR (time domain reflectivity) probes; the reliability of this forecasting system and its benefits were assessed in the 2012 growing season. The results obtained show how the proposed drought forecasting system is able to have a high reliability of forecast at least for 7-10 days ahead of time.

Providing Real-time Sea Ice Modeling Support to the U.S. Coast Guard

NASA Astrophysics Data System (ADS)

Allard, Richard; Dykes, James; Hebert, David; Posey, Pamela; Rogers, Erick; Wallcraft, Alan; Phelps, Michael; Smedstad, Ole Martin; Wang, Shouping; Geiszler, Dan

2016-04-01

The Naval Research Laboratory (NRL) supported the U.S. Coast Guard Research Development Center (RDC) through a demonstration project during the summer and autumn of 2015. Specifically, a modeling system composed of a mesoscale atmospheric model, regional sea ice model, and regional wave model were loosely coupled to provide real-time 72-hr forecasts of environmental conditions for the Beaufort/Chukchi Seas. The system components included a 2-km regional Community Ice CodE (CICE) sea ice model, 15-km Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS) atmospheric model, and a 5-km regional WAVEWATCH III wave model. The wave model utilized modeled sea ice concentration fields to incorporate the effects of sea ice on waves. The other modeling components assimilated atmosphere, ocean, and ice observations available from satellite and in situ sources. The modeling system generated daily 72-hr forecasts of synoptic weather (including visibility), ice drift, ice thickness, ice concentration and ice strength for missions within the economic exclusion zone off the coast of Alaska and a transit to the North Pole in support of the National Science Foundation GEOTRACES cruise. Model forecasts graphics were shared on a common web page with selected graphical products made available via ftp for bandwidth limited users. Model ice thickness and ice drift show very good agreement compared with Cold Regions Research and Engineering Laboratory (CRREL) Ice Mass Balance buoys. This demonstration served as a precursor to a fully coupled atmosphere-ocean-wave-ice modeling system under development. National Ice Center (NIC) analysts used these model data products (CICE and COAMPS) along with other existing model and satellite data to produce the predicted 48-hr position of the ice edge. The NIC served as a liaison with the RDC and NRL to provide feedback on the model predictions. This evaluation provides a baseline analysis of the current models for future comparison studies with the fully coupled modeling system.

Skillful regional prediction of Arctic sea ice on seasonal timescales

NASA Astrophysics Data System (ADS)

Bushuk, Mitchell; Msadek, Rym; Winton, Michael; Vecchi, Gabriel A.; Gudgel, Rich; Rosati, Anthony; Yang, Xiaosong

2017-05-01

Recent Arctic sea ice seasonal prediction efforts and forecast skill assessments have primarily focused on pan-Arctic sea ice extent (SIE). In this work, we move toward stakeholder-relevant spatial scales, investigating the regional forecast skill of Arctic sea ice in a Geophysical Fluid Dynamics Laboratory (GFDL) seasonal prediction system. Using a suite of retrospective initialized forecasts spanning 1981-2015 made with a coupled atmosphere-ocean-sea ice-land model, we show that predictions of detrended regional SIE are skillful at lead times up to 11 months. Regional prediction skill is highly region and target month dependent and generically exceeds the skill of an anomaly persistence forecast. We show for the first time that initializing the ocean subsurface in a seasonal prediction system can yield significant regional skill for winter SIE. Similarly, as suggested by previous work, we find that sea ice thickness initial conditions provide a crucial source of skill for regional summer SIE.

Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

NASA Astrophysics Data System (ADS)

van der Zwan, Rene

2013-04-01

The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water management, including temporary lower storage basin levels and a reduction in extra investments for infrastructural measures.

GEOS S2S-2_1: GMAO's New High Resolution Seasonal Prediction System

NASA Technical Reports Server (NTRS)

Molod, Andrea; Akella, Santha; Andrews, Lauren; Barahona, Donifan; Borovikov, Anna; Chang, Yehui; Cullather, Richard; Hackert, Eric; Kovach, Robin; Koster, Randal;

ENSO Prediction in the NASA GMAO GEOS-5 Seasonal Forecasting System

NASA Astrophysics Data System (ADS)

Kovach, R. M.; Borovikov, A.; Marshak, J.; Pawson, S.; Vernieres, G.

2016-12-01

Seasonal-to-Interannual coupled forecasts are conducted in near-real time with the Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model (AOGCM). A 30-year suite of 9-month hindcasts is available, initialized with the MERRA-Ocean, MERRA-Land, and MERRA atmospheric fields. These forecasts are used to predict the timing and magnitude of ENSO and other short-term climate variability. The 2015 El Niño peaked in November 2015 and was considered a "very strong" event with the Equatorial Pacific Ocean sea-surface-temperature (SST) anomalies higher than 2.0 °C. These very strong temperature anomalies began in Sep/Oct/Nov (SON) of 2015 and persisted through Dec/Jan/Feb (DJF) of 2016. The other two very strong El Niño events recently recorded occurred in 1981/82 and 1997/98. The GEOS-5 system began predicting a very strong El Niño for SON starting with the March 2015 forecast. At this time, the GMAO forecast was an outlier in both the NMME and IRI multi-model ensemble prediction plumes. The GMAO May 2015 forecast for the November 2015 peak in temperature anomaly in the Niño3.4 region was in excellent agreement with the real event, but in May this forecast was still one of the outliers in the multi-model forecasts. The GEOS-5 May 2015 forecast also correctly predicted the weakening of the Eastern Pacific (Niño1+2) anomalies for SON. We will present a summary of the NASA GMAO GEOS-5 Seasonal Forecast System skills based on historic hindcasts. Initial conditions, prediction of ocean surface and subsurface evolution for the 2015/16 El Niño will be compared to the 1998/97 event. GEOS-5 capability to predict the precipitation, i.e. to model the teleconnection patterns associated with El Niño will also be shown. To conclude, we will highlight some new developments in the GEOS forecasting system.

Ensemble forecast of human West Nile virus cases and mosquito infection rates

NASA Astrophysics Data System (ADS)

Defelice, Nicholas B.; Little, Eliza; Campbell, Scott R.; Shaman, Jeffrey

2017-02-01

West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001-2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.

Short term load forecasting using a self-supervised adaptive neural network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, H.; Pimmel, R.L.

The authors developed a self-supervised adaptive neural network to perform short term load forecasts (STLF) for a large power system covering a wide service area with several heavy load centers. They used the self-supervised network to extract correlational features from temperature and load data. In using data from the calendar year 1993 as a test case, they found a 0.90 percent error for hour-ahead forecasting and 1.92 percent error for day-ahead forecasting. These levels of error compare favorably with those obtained by other techniques. The algorithm ran in a couple of minutes on a PC containing an Intel Pentium --more » 120 MHz CPU. Since the algorithm included searching the historical database, training the network, and actually performing the forecasts, this approach provides a real-time, portable, and adaptable STLF.« less

Ensemble forecast of human West Nile virus cases and mosquito infection rates.

PubMed

DeFelice, Nicholas B; Little, Eliza; Campbell, Scott R; Shaman, Jeffrey

2017-02-24

West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001-2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.

Forecasting of global solar radiation using anfis and armax techniques

NASA Astrophysics Data System (ADS)

Muhammad, Auwal; Gaya, M. S.; Aliyu, Rakiya; Aliyu Abdulkadir, Rabi'u.; Dauda Umar, Ibrahim; Aminu Yusuf, Lukuman; Umar Ali, Mudassir; Khairi, M. T. M.

2018-01-01

Procurement of measuring device, maintenance cost coupled with calibration of the instrument contributed to the difficulty in forecasting of global solar radiation in underdeveloped countries. Most of the available regressional and mathematical models do not capture well the behavior of the global solar radiation. This paper presents the comparison of Adaptive Neuro Fuzzy Inference System (ANFIS) and Autoregressive Moving Average with eXogenous term (ARMAX) in forecasting global solar radiation. Full-Scale (experimental) data of Nigerian metrological agency, Sultan Abubakar III international airport Sokoto was used to validate the models. The simulation results demonstrated that the ANFIS model having achieved MAPE of 5.34% outperformed the ARMAX model. The ANFIS could be a valuable tool for forecasting the global solar radiation.

GEOS S2S-2_1: The GMAO new high resolution Seasonal Prediction System

NASA Astrophysics Data System (ADS)

Molod, A.; Vikhliaev, Y. V.; Hackert, E. C.; Kovach, R. M.; Zhao, B.; Cullather, R. I.; Marshak, J.; Borovikov, A.; Li, Z.; Barahona, D.; Andrews, L. C.; Chang, Y.; Schubert, S. D.; Koster, R. D.; Suarez, M.; Akella, S.

2017-12-01

A new version of the modeling and analysis system used to produce subseasonalto seasonal forecasts has just been released by the NASA/Goddard GlobalModeling and Assimilation Office. The new version runs at higher atmospheric resolution (approximately 1/2 degree globally), contains a subtantially improvedmodel description of the cryosphere, and includes additional interactive earth system model components (aerosol model). In addition, the Ocean data assimilationsystem has been replaced with a Local Ensemble Transform Kalman Filter.Here will describe the new system, along with the plans for the future (GEOS S2S-3_0) which will include a higher resolution ocean model and more interactive earth system model components (interactive vegetation, biomass burning from fires). We will alsopresent results from a free-running coupled simulation with the new system and resultsfrom a series of retrospective seasonal forecasts.Results from retrospective forecasts show significant improvements in surface temperaturesover much of the northern hemisphere and a much improved prediction of sea ice extent in bothhemispheres. The precipitation forecast skill is comparable to previous S2S systems, andthe only tradeoff is an increased "double ITCZ", which is expected as we go to higher atmospheric resolution.

Aerosols and Aerosol-related haze forecasting in China Meteorological Adminstration

NASA Astrophysics Data System (ADS)

Zhou, Chunhong; Zhang, Xiaoye; Gong, Sunling; Liu, Hongli; Xue, Min

2017-04-01

CMA Unified Atmospheric Chemistry Environmental Forecasting System (CUACE) is a unified numerical chemical weather forecasting system with BC, OC, Sulfate, Nitrate, Ammonia, Dust and Sea-Salt aerosols and their sources, gas to particle processes, SOA, microphysics and transformation. With an open interface, CUACE has been online coupled to mesoscale model MM5 and the new NWP system GRAPES (Global/Regional Assimilation and Prediction Enhanced System)min CMA. With Chinese Emissions from Cao and Zhang(2012 and 2013), a forecasting system called CUACE/Haze-fog has been running in real time in CMA and issue 5-days PM10, O3 and Visibility forecasts. A comprehensive ACI scheme has also been developed in CUACE Calculated by a sectional aerosol activation scheme based on the information of size and mass from CUACE and the thermal-dynamic and humid states from the weather model at each time step, the cloud condensation nuclei (CCN) is fed online interactively into a two-moment cloud scheme (WDM6) and a convective parameterization to drive the cloud physics and precipitation formation processes. The results show that interactive aerosols with the WDM6 in CUACE obviously improve the clouds properties and the precipitation, showing 24% to 48% enhancements of TS scoring for 6-h precipitation .

OS Aviation Information

Science.gov Websites

Aviation Weather Program is to couple the art and science of meteorology to enhance the safe and efficient significant weather forecasts crossing international boundaries. Keeping Our National Airspace System Safe The System Newsletter Aviation Weather Center (AWC) Alaska Aviation Weather Unit (AAWU) Space Environment

Coastal and Riverine Flood Forecast Model powered by ADCIRC

NASA Astrophysics Data System (ADS)

Khalid, A.; Ferreira, C.

2017-12-01

Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which might provide better and more reliable forecast for the flood affected communities.

An Experimental Real-Time Ocean Nowcast/Forecast System for Intra America Seas

NASA Astrophysics Data System (ADS)

Ko, D. S.; Preller, R. H.; Martin, P. J.

2003-04-01

An experimental real-time Ocean Nowcast/Forecast System has been developed for the Intra America Seas (IASNFS). The area of coverage includes the Caribbean Sea, the Gulf of Mexico and the Straits of Florida. The system produces nowcast and up to 72 hours forecast the sea level variation, 3D ocean current, temperature and salinity fields. IASNFS consists an 1/24 degree (~5 km), 41-level sigma-z data-assimilating ocean model based on NCOM. For daily nowcast/forecast the model is restarted from previous nowcast. Once model is restarted it continuously assimilates the synthetic temperature/salinity profiles generated by a data analysis model called MODAS to produce nowcast. Real-time data come from satellite altimeter (GFO, TOPEX/Poseidon, ERS-2) sea surface height anomaly and AVHRR sea surface temperature. Three hourly surface heat fluxes, including solar radiation, wind stresses and sea level air pressure from NOGAPS/FNMOC are applied for surface forcing. Forecasts are produced with available NOGAPS forecasts. Once the nowcast/forecast are produced they are distributed through the Internet via the updated web pages. The open boundary conditions including sea surface elevation, transport, temperature, salinity and currents are provided by the NRL 1/8 degree Global NCOM which is operated daily. An one way coupling scheme is used to ingest those boundary conditions into the IAS model. There are 41 rivers with monthly discharges included in the IASNFS.

Evaluation of Regional Extended-Range Prediction for Tropical Waves Using COAMPS®

NASA Astrophysics Data System (ADS)

Hong, X.; Reynolds, C. A.; Doyle, J. D.; May, P. W.; Chen, S.; Flatau, M. K.; O'Neill, L. W.

2014-12-01

The Navy's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS1) in a two-way coupled mode is used for two-month regional extended-range prediction for the Madden-Julian Oscillation (MJO) and Tropical Cyclone 05 (TC05) that occurred during the DYNAMO period from November to December 2011. Verification and statistics from two experiments with 45-km and 27-km horizontal resolutions indicate that 27-km run provides a better representation of the three MJO events that occurred during this 2-month period, including the two convectively-coupled Kelvin waves associated with the second MJO event as observed. The 27-km run also significantly reduces forecast error after 15-days, reaching a maximum bias reduction of 89% in the third 15-day period due to the well represented MJO propagation over the Maritime Continent. Correlations between the model forecasts and observations or ECMWF analyses show that the MJO suppressed period is more difficult to predict than the active period. In addition, correlation coefficients for cloud liquid water path (CLWP) and precipitation are relatively low for both cases compared to other variables. The study suggests that a good simulation of TC05 and a good simulation of the Kelvin waves and westerly wind bursts are linked. Further research is needed to investigate the capability in regional extended-range forecasts when the lateral boundary conditions are provided from a long-term global forecast to allow for an assessment of potential operational forecast skill. _____________________________________________________ 1COAMPS is a registered trademark of U.S. Naval Research Laboratory

Framework of distributed coupled atmosphere-ocean-wave modeling system

NASA Astrophysics Data System (ADS)

Wen, Yuanqiao; Huang, Liwen; Deng, Jian; Zhang, Jinfeng; Wang, Sisi; Wang, Lijun

2006-05-01

In order to research the interactions between the atmosphere and ocean as well as their important role in the intensive weather systems of coastal areas, and to improve the forecasting ability of the hazardous weather processes of coastal areas, a coupled atmosphere-ocean-wave modeling system has been developed. The agent-based environment framework for linking models allows flexible and dynamic information exchange between models. For the purpose of flexibility, portability and scalability, the framework of the whole system takes a multi-layer architecture that includes a user interface layer, computational layer and service-enabling layer. The numerical experiment presented in this paper demonstrates the performance of the distributed coupled modeling system.

Improving Subtropical Boundary Layer Cloudiness in the 2011 NCEP GFS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fletcher, J. K.; Bretherton, Christopher S.; Xiao, Heng

2014-09-23

The current operational version of National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) shows significant low cloud bias. These biases also appear in the Coupled Forecast System (CFS), which is developed from the GFS. These low cloud biases degrade seasonal and longer climate forecasts, particularly of short-wave cloud radiative forcing, and affect predicted sea surface temperature. Reducing this bias in the GFS will aid the development of future CFS versions and contributes to NCEP's goal of unified weather and climate modelling. Changes are made to the shallow convection and planetary boundary layer parameterisations to make them more consistentmore » with current knowledge of these processes and to reduce the low cloud bias. These changes are tested in a single-column version of GFS and in global simulations with GFS coupled to a dynamical ocean model. In the single-column model, we focus on changing parameters that set the following: the strength of shallow cumulus lateral entrainment, the conversion of updraught liquid water to precipitation and grid-scale condensate, shallow cumulus cloud top, and the effect of shallow convection in stratocumulus environments. Results show that these changes improve the single-column simulations when compared to large eddy simulations, in particular through decreasing the precipitation efficiency of boundary layer clouds. These changes, combined with a few other model improvements, also reduce boundary layer cloud and albedo biases in global coupled simulations.« less

The Met Office Coupled Atmosphere/Land/Ocean/Sea-Ice Data Assimilation System

NASA Astrophysics Data System (ADS)

Lea, Daniel; Mirouze, Isabelle; Martin, Matthew; Hines, Adrian; Guiavarch, Catherine; Shelly, Ann

2014-05-01

The Met Office has developed a weakly-coupled data assimilation (DA) system using the global coupled model HADGEM3 (Hadley Centre Global Environment Model, version 3). This model combines the atmospheric model UM (Unified Model) at 60 km horizontal resolution on 85 vertical levels, the ocean model NEMO (Nucleus for European Modeling of the Ocean) at 25 km (at the equator) horizontal resolution on 75 vertical levels, and the sea-ice model CICE at the same resolution as NEMO. The atmosphere and the ocean/sea-ice fields are coupled every 1-hour using the OASIS coupler. The coupled model is corrected using two separate 6-hour window data assimilation systems: a 4D-Var for the atmosphere with associated soil moisture content nudging and snow analysis schemes on the one hand, and a 3D-Var FGAT for the ocean and sea-ice on the other hand. The background information in the DA systems comes from a previous 6-hour forecast of the coupled model. To show the impact of coupled DA, one-month experiments have been carried out, including 1) a full atmosphere/land/ocean/sea-ice coupled DA run, 2) an atmosphere-only run forced by OSTIA SSTs and sea-ice with atmosphere and land DA, and 3) an ocean-only run forced by atmospheric fields from run 2 with ocean and sea-ice DA. In addition, 5-day forecast runs, started twice a day, have been produced from initial conditions generated by either run 1 or a combination of runs 2 and 3. The different results have been compared to each other and, whenever possible, to other references such as the Met Office atmosphere and ocean operational analyses or the OSTIA data. These all show the coupled DA system functioning well. Evidence of imbalances and initialisation shocks has also been looked for.

Development and Use of the Hydrologic Ensemble Forecast System by the National Weather Service to Support the New York City Water Supply

NASA Astrophysics Data System (ADS)

Shedd, R.; Reed, S. M.; Porter, J. H.

2015-12-01

The National Weather Service (NWS) has been working for several years on the development of the Hydrologic Ensemble Forecast System (HEFS). The objective of HEFS is to provide ensemble river forecasts incorporating the best precipitation and temperature forcings at any specific time horizon. For the current implementation, this includes the Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFSv2). One of the core partners that has been working with the NWS since the beginning of the development phase of HEFS is the New York City Department of Environmental Protection (NYCDEP) which is responsible for the complex water supply system for New York City. The water supply system involves a network of reservoirs in both the Delaware and Hudson River basins. At the same time that the NWS was developing HEFS, NYCDEP was working on enhancing the operations of their water supply reservoirs through the development of a new Operations Support Tool (OST). OST is designed to guide reservoir system operations to ensure an adequate supply of high-quality drinking water for the city, as well as to meet secondary objectives for reaches downstream of the reservoirs assuming the primary water supply goals can be met. These secondary objectives include fisheries and ecosystem support, enhanced peak flow attenuation beyond that provided natively by the reservoirs, salt front management, and water supply for other cities. Since January 2014, the NWS Northeast and Middle Atlantic River Forecast Centers have provided daily one year forecasts from HEFS to NYCDEP. OST ingests these forecasts, couples them with near-real-time environmental and reservoir system data, and drives models of the water supply system. The input of ensemble forecasts results in an ensemble of model output, from which information on the range and likelihood of possible future system states can be extracted. This type of probabilistic information provides system managers with additional information not available from deterministic forecasts and allows managers to better assess risk, and provides greater context for decision-making than has been available in the past. HEFS has allowed NYCDEP water supply managers to make better decisions on reservoir operations than they likely would have in the past, using only deterministic forecasts.

Assessment of an ensemble seasonal streamflow forecasting system for Australia

NASA Astrophysics Data System (ADS)

Bennett, James C.; Wang, Quan J.; Robertson, David E.; Schepen, Andrew; Li, Ming; Michael, Kelvin

2017-11-01

Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean-land-atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall-runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( < 4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall-runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall-runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.

Characteristics of Operational Space Weather Forecasting: Observations and Models

NASA Astrophysics Data System (ADS)

Berger, Thomas; Viereck, Rodney; Singer, Howard; Onsager, Terry; Biesecker, Doug; Rutledge, Robert; Hill, Steven; Akmaev, Rashid; Milward, George; Fuller-Rowell, Tim

2015-04-01

In contrast to research observations, models and ground support systems, operational systems are characterized by real-time data streams and run schedules, with redundant backup systems for most elements of the system. We review the characteristics of operational space weather forecasting, concentrating on the key aspects of ground- and space-based observations that feed models of the coupled Sun-Earth system at the NOAA/Space Weather Prediction Center (SWPC). Building on the infrastructure of the National Weather Service, SWPC is working toward a fully operational system based on the GOES weather satellite system (constant real-time operation with back-up satellites), the newly launched DSCOVR satellite at L1 (constant real-time data network with AFSCN backup), and operational models of the heliosphere, magnetosphere, and ionosphere/thermosphere/mesophere systems run on the Weather and Climate Operational Super-computing System (WCOSS), one of the worlds largest and fastest operational computer systems that will be upgraded to a dual 2.5 Pflop system in 2016. We review plans for further operational space weather observing platforms being developed in the context of the Space Weather Operations Research and Mitigation (SWORM) task force in the Office of Science and Technology Policy (OSTP) at the White House. We also review the current operational model developments at SWPC, concentrating on the differences between the research codes and the modified real-time versions that must run with zero fault tolerance on the WCOSS systems. Understanding the characteristics and needs of the operational forecasting community is key to producing research into the coupled Sun-Earth system with maximal societal benefit.

Development and Implementation of Dynamic Scripts to Execute Cycled WRF/GSI Forecasts

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Berndt, Emily; Li, Quanli; Watson, Leela

2014-01-01

Automating the coupling of data assimilation (DA) and modeling systems is a unique challenge in the numerical weather prediction (NWP) research community. In recent years, the Development Testbed Center (DTC) has released well-documented tools such as the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolation (GSI) DA system that can be easily downloaded, installed, and run by researchers on their local systems. However, developing a coupled system in which the various preprocessing, DA, model, and postprocessing capabilities are all integrated can be labor-intensive if one has little experience with any of these individual systems. Additionally, operational modeling entities generally have specific coupling methodologies that can take time to understand and develop code to implement properly. To better enable collaborating researchers to perform modeling and DA experiments with GSI, the Short-term Prediction Research and Transition (SPoRT) Center has developed a set of Perl scripts that couple GSI and WRF in a cycling methodology consistent with the use of real-time, regional observation data from the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC). Because Perl is open source, the code can be easily downloaded and executed regardless of the user's native shell environment. This paper will provide a description of this open-source code and descriptions of a number of the use cases that have been performed by SPoRT collaborators using the scripts on different computing systems.

Data Assimilation as it Relates to the Sea Ice Outlook (SIO) and Prospects for Improvement

NASA Technical Reports Server (NTRS)

Cullather, Richard

2017-01-01

Improved seasonal forecasts of Arctic sea ice are important for regional stakeholders, but also for obtaining a better understanding of the Arctic climate system. An important part of the forecasts is the initial sea ice, ocean, and atmosphere initial conditions. I briefly give an overview of the initial conditions currently being used in seasonal sea ice predictions. I also identify available sources of observational data and prospects for coupled atmosphere/ocean reanalyses.

Simulation studies of the application of SEASAT data in weather and state of sea forecasting models

NASA Technical Reports Server (NTRS)

Cardone, V. J.; Greenwood, J. A.

1979-01-01

The design and analysis of SEASAT simulation studies in which the error structure of conventional analyses and forecasts is modeled realistically are presented. The development and computer implementation of a global spectral ocean wave model is described. The design of algorithms for the assimilation of theoretical wind data into computers and for the utilization of real wind data and wave height data in a coupled computer system are presented.

Exploring the calibration of a wind forecast ensemble for energy applications

NASA Astrophysics Data System (ADS)

Heppelmann, Tobias; Ben Bouallegue, Zied; Theis, Susanne

2015-04-01

In the German research project EWeLiNE, Deutscher Wetterdienst (DWD) and Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) are collaborating with three German Transmission System Operators (TSO) in order to provide the TSOs with improved probabilistic power forecasts. Probabilistic power forecasts are derived from probabilistic weather forecasts, themselves derived from ensemble prediction systems (EPS). Since the considered raw ensemble wind forecasts suffer from underdispersiveness and bias, calibration methods are developed for the correction of the model bias and the ensemble spread bias. The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 21 h delivered by the convection permitting high-resolution ensemble system COSMO-DE-EPS which has become operational in 2012 at DWD. The ensemble consists of 20 ensemble members driven by four different global models. The model area includes whole Germany and parts of Central Europe with a horizontal resolution of 2.8 km and a vertical resolution of 50 model levels. For verification we use wind mast measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts can be performed by different statistical methods applied to the raw ensemble output. Here, we explore local bivariate Ensemble Model Output Statistics at individual sites and quantile regression with different predictors. Applying different methods, we already show an improvement of ensemble wind forecasts from COSMO-DE-EPS for energy applications. In addition, an ensemble copula coupling approach transfers the time-dependencies of the raw ensemble to the calibrated ensemble. The calibrated wind forecasts are evaluated first with univariate probabilistic scores and additionally with diagnostics of wind ramps in order to assess the time-consistency of the calibrated ensemble members.

Evaluation of Model Performance over the Maritime Continent

NASA Astrophysics Data System (ADS)

Reynolds, C. A.; Barton, N. P.; Chen, S.; Flatau, M. K.; Ridout, J. A.; Janiga, M.; Jensen, T.; Richman, J. G.; Metzger, E. J.; Baranowski, D.

2017-12-01

The introduction of high-resolution global coupled models holds promise for extended-range (subseasonal to seasonal) prediction of high-impact weather. While forecast models have shown considerable improvement in the prediction of tropical phenomena on these timescales, specifically in the simulation and prediction of the Madden-Julian Oscillation (MJO), obstacles remain. In particular, many models still have difficulty accurately simulating the propagation of the MJO over the maritime continent. This has been hypothesized, at least in part, to be related to deficiencies in simulating the diurnal cycle over this region, which in turn is dependent on accurate representation of fine-scale atmosphere-ocean-land interactions, orography, and atmospheric convection. These issues have motivated the international Year of Maritime Continent (YMC) effort and the Office of Naval Research Propagation of Intra-Seasonal Tropical Oscillations (PISTON) initiative. In preparation for YMC and PISTON, we closely evaluate the performance of the Navy Earth System Model (NESM), a coupled global forecast model, in representing the diurnal cycle and other prominent phenomena in the maritime continent region. NESM performance is compared with stand-alone atmospheric simulations with prescribed fixed and analyzed sea surface temperatures (SSTs). Initial results from the Dynamics of the Madden-Julian Oscillation field phase (Fall 2011) period indicate that NESM is able to capture the precipitation day-time maximum over land and night-time maximum over ocean, but day-time precipitation over Borneo, Sumatra and the Malay Peninsula is too strong as compared to TRMM observations. The simulation of low-level winds qualitatively captures sea and land breeze patterns as compared with ERA-Interim analysis, with quantitative biases varying by island. The fully-coupled system and the stand-alone atmospheric model simulations are more similar to each other than to the observations, indicating that active ocean coupling is not the most prominent issue contributing to biases in these simulations. The performance of NESM will be more thoroughly evaluated and compared to other forecast systems using the 45-day forecasts currently being produced four times per week for the 1999-2015 time period under the NOAA SubX project.

Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model

NASA Astrophysics Data System (ADS)

Zheng, Fei; Zhu, Jiang

2017-04-01

How to design a reliable ensemble prediction strategy with considering the major uncertainties of a forecasting system is a crucial issue for performing an ensemble forecast. In this study, a new stochastic perturbation technique is developed to improve the prediction skills of El Niño-Southern Oscillation (ENSO) through using an intermediate coupled model. We first estimate and analyze the model uncertainties from the ensemble Kalman filter analysis results through assimilating the observed sea surface temperatures. Then, based on the pre-analyzed properties of model errors, we develop a zero-mean stochastic model-error model to characterize the model uncertainties mainly induced by the missed physical processes of the original model (e.g., stochastic atmospheric forcing, extra-tropical effects, Indian Ocean Dipole). Finally, we perturb each member of an ensemble forecast at each step by the developed stochastic model-error model during the 12-month forecasting process, and add the zero-mean perturbations into the physical fields to mimic the presence of missing processes and high-frequency stochastic noises. The impacts of stochastic model-error perturbations on ENSO deterministic predictions are examined by performing two sets of 21-yr hindcast experiments, which are initialized from the same initial conditions and differentiated by whether they consider the stochastic perturbations. The comparison results show that the stochastic perturbations have a significant effect on improving the ensemble-mean prediction skills during the entire 12-month forecasting process. This improvement occurs mainly because the nonlinear terms in the model can form a positive ensemble-mean from a series of zero-mean perturbations, which reduces the forecasting biases and then corrects the forecast through this nonlinear heating mechanism.

Impact of Land Surface Initialization Approach on Subseasonal Forecast Skill: a Regional Analysis in the Southern Hemisphere

NASA Technical Reports Server (NTRS)

Hirsch, Annette L.; Kala, Jatin; Pitman, Andy J.; Carouge, Claire; Evans, Jason P.; Haverd, Vanessa; Mocko, David

2014-01-01

The authors use a sophisticated coupled land-atmosphere modeling system for a Southern Hemisphere subdomain centered over southeastern Australia to evaluate differences in simulation skill from two different land surface initialization approaches. The first approach uses equilibrated land surface states obtained from offline simulations of the land surface model, and the second uses land surface states obtained from reanalyses. The authors find that land surface initialization using prior offline simulations contribute to relative gains in subseasonal forecast skill. In particular, relative gains in forecast skill for temperature of 10%-20% within the first 30 days of the forecast can be attributed to the land surface initialization method using offline states. For precipitation there is no distinct preference for the land surface initialization method, with limited gains in forecast skill irrespective of the lead time. The authors evaluated the asymmetry between maximum and minimum temperatures and found that maximum temperatures had the largest gains in relative forecast skill, exceeding 20% in some regions. These results were statistically significant at the 98% confidence level at up to 60 days into the forecast period. For minimum temperature, using reanalyses to initialize the land surface contributed to relative gains in forecast skill, reaching 40% in parts of the domain that were statistically significant at the 98% confidence level. The contrasting impact of the land surface initialization method between maximum and minimum temperature was associated with different soil moisture coupling mechanisms. Therefore, land surface initialization from prior offline simulations does improve predictability for temperature, particularly maximum temperature, but with less obvious improvements for precipitation and minimum temperature over southeastern Australia.

Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, C. W.; Hood, Raleigh R.; Long, Wen

The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic empirical approach, whereby real-time output from the coupled physical biogeochemical model drives multivariate empirical habitat modelsmore » of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanistic–empirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken.« less

Performance analysis of coupled and uncoupled hydrodynamic and wave models in the northern Adriatic Sea

NASA Astrophysics Data System (ADS)

Busca, Claudia; Coluccelli, Alessandro; Valentini, Andrea; Benetazzo, Alvise; Bonaldo, Davide; Bortoluzzi, Giovanni; Carniel, Sandro; Falcieri, Francesco; Paccagnella, Tiziana; Ravaioli, Mariangela; Riminucci, Francesco; Sclavo, Mauro; Russo, Aniello

2014-05-01

The complex dynamics of the Adriatic Sea are the result of geographical position, orography and bathymetry, as well as rivers discharge and meteorological conditions that influence, more strongly, the shallow northern part. Such complexity requires a constant monitoring of marine conditions in order to support several activities (marine resources management, naval operations, emergency management, shipping, tourism, as well as scientific ones). Platforms, buoys and mooring located in Adriatic Sea supply almost continuously real time punctual information, which can be spatially extended, with some limitations, by drifters and remote sensing. Operational forecasting systems represent valid tools to provide a complete tridimensional coverage of the area, with a high spatial and temporal resolution. The Hydro-Meteo-Clima Service of the Emilia-Romagna Environmental Agency (ARPA-SIMC, Bologna, Italy) and the Dept. of Life and Environmental Sciences of Università Politecnica delle Marche (DISVA-UNIVPM, Ancona, Italy), in collaboration with the Institute of Marine Science of the National Research Council (ISMAR-CNR, Italy) operationally run several wave and hydrodynamic models on the Adriatic Sea. The main implementations are based on the Regional Ocean Modeling System (ROMS), the wave model Simulating WAves Nearshore (SWAN), and the coupling of the former two models in the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) system. Horizontal resolutions of the different systems range from the 2 km of AdriaROMS to the 0.5 km of the recently implemented northern Adriatic COAWST. Forecasts are produced every day for the subsequent 72 hour with hourly resolution. All the systems compute the fluxes exchanged through the interface with the atmosphere from the numerical weather prediction system named COSMO-I7, an implementation for Italy of the Consortium for Small-scale Modeling (COSMO) model, at 7 km horizontal resolution. Considering the several operational implementations currently running, there is the need to: assess their forecast skill; quantitatively evaluate if the new, coupled systems provide better performances than the uncoupled ones; individuate weaknesses and eventual time trends in the forecasts quality, their causes, and actions to improve the systems. This work presents a first effort aimed to satisfy such need. We employ in situ and remote sensing data collected starting from November 2011, in particular: temperature and salinity data collected during several oceanographic cruises, sea surface temperature derived from satellite measurements, waves, sea level and currents measurements from oceanographic buoys and platforms; specific observational activities funded by the Italian Flagship project RITMARE allowed to collect new measurements in NA coastal areas. Data-model comparison is firstly performed with exploratory qualitative comparisons in order to highlight discrepancies between observed and forecasted data, then a quantitative comparison is performed through the computation of standard statistical scores (root mean square error, mean error, mean bias, standard deviation, cross-correlation). Results are plotted in Taylor diagrams for a rapid evaluation of the overall performances.

Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System

PubMed Central

Katz, Graham; Summers, Kristen; Ackermann, Chris; Zavorin, Ilya; Lim, Zunsik; Muthiah, Sathappan; Butler, Patrick; Self, Nathan; Zhao, Liang; Lu, Chang-Tien; Khandpur, Rupinder Paul; Fayed, Youssef; Ramakrishnan, Naren

2014-01-01

Abstract Developed under the Intelligence Advanced Research Project Activity Open Source Indicators program, Early Model Based Event Recognition using Surrogates (EMBERS) is a large-scale big data analytics system for forecasting significant societal events, such as civil unrest events on the basis of continuous, automated analysis of large volumes of publicly available data. It has been operational since November 2012 and delivers approximately 50 predictions each day for countries of Latin America. EMBERS is built on a streaming, scalable, loosely coupled, shared-nothing architecture using ZeroMQ as its messaging backbone and JSON as its wire data format. It is deployed on Amazon Web Services using an entirely automated deployment process. We describe the architecture of the system, some of the design tradeoffs encountered during development, and specifics of the machine learning models underlying EMBERS. We also present a detailed prospective evaluation of EMBERS in forecasting significant societal events in the past 2 years. PMID:25553271

Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System.

PubMed

Doyle, Andy; Katz, Graham; Summers, Kristen; Ackermann, Chris; Zavorin, Ilya; Lim, Zunsik; Muthiah, Sathappan; Butler, Patrick; Self, Nathan; Zhao, Liang; Lu, Chang-Tien; Khandpur, Rupinder Paul; Fayed, Youssef; Ramakrishnan, Naren

2014-12-01

Developed under the Intelligence Advanced Research Project Activity Open Source Indicators program, Early Model Based Event Recognition using Surrogates (EMBERS) is a large-scale big data analytics system for forecasting significant societal events, such as civil unrest events on the basis of continuous, automated analysis of large volumes of publicly available data. It has been operational since November 2012 and delivers approximately 50 predictions each day for countries of Latin America. EMBERS is built on a streaming, scalable, loosely coupled, shared-nothing architecture using ZeroMQ as its messaging backbone and JSON as its wire data format. It is deployed on Amazon Web Services using an entirely automated deployment process. We describe the architecture of the system, some of the design tradeoffs encountered during development, and specifics of the machine learning models underlying EMBERS. We also present a detailed prospective evaluation of EMBERS in forecasting significant societal events in the past 2 years.

Timetable of an operational flood forecasting system

NASA Astrophysics Data System (ADS)

Liechti, Katharina; Jaun, Simon; Zappa, Massimiliano

2010-05-01

At present a new underground part of Zurich main station is under construction. For this purpose the runoff capacity of river Sihl, which is passing beneath the main station, is reduced by 40%. If a flood is to occur the construction site is evacuated and gates can be opened for full runoff capacity to prevent bigger damages. However, flooding the construction site, even if it is controlled, is coupled with costs and retardation. The evacuation of the construction site at Zurich main station takes about 2 to 4 hours and opening the gates takes another 1 to 2 hours each. In the upper part of the 336 km2 Sihl catchment the Sihl lake, a reservoir lake, is situated. It belongs and is used by the Swiss Railway Company for hydropower production. This lake can act as a retention basin for about 46% of the Sihl catchment. Lowering the lake level to gain retention capacity, and therewith safety, is coupled with direct loss for the Railway Company. To calculate the needed retention volume and the water to be released facing unfavourable weather conditions, forecasts with a minimum lead time of 2 to 3 days are needed. Since the catchment is rather small, this can only be realised by the use of meteorological forecast data. Thus the management of the construction site depends on accurate forecasts to base their decisions on. Therefore an operational hydrological ensemble prediction system (HEPS) was introduced in September 2008 by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). It delivers daily discharge forecasts with a time horizon of 5 days. The meteorological forecasts are provided by MeteoSwiss and stem from the operational limited-area COSMO-LEPS which downscales the ECMWF ensemble prediction system to a spatial resolution of 7 km. Additional meteorological data for model calibration and initialisation (air temperature, precipitation, water vapour pressure, global radiation, wind speed and sunshine duration) and radar data are also provided by MeteoSwiss. Additional meteorological and hydrological observations are provided by a hydropower company, the Canton of Zurich and the Federal Office for the Environment (FOEN). The hydrological forecasting is calculated by the semi-distributed hydrological model PREVAH (Precipitation-Runoff-EVapotranspiration-HRU-related Model) and is further processed by the hydraulic model FLORIS. Finally the forecasts and alerts along with additional meteorological and hydrological observations and forecasts from collaborating institution are sent to a webserver accessible for decision makers. We will document the setup of our operational flood forecasting system, evaluate its performance and show how the collaboration and communication between science and practice, including all the different interests, works for this particular example.

Combining Satellite Ocean Color Imagery and Circulation Modeling to Forecast Bio-Optical Properties: Comparison of Models and Advection Schemes

DTIC Science & Technology

2008-10-01

Director NCST E. R. Franchi , 7000 ^^M^4^k ro£— 4// 2^/s y Public Affairs (Unclassified/ Unlimited Only), Code 7030 4 Division, Code Author, Code...from the Navy Operational Global Atmospheric Prediction System (NOGAPS, Hogan and Rosmond, 1991) and assimilates data via the Navy Coupled Ocean...forecasts using Global , Atlantic, Gulf of Mexico, and northern Gulf of Mexico configurations of HYCOM. Proceedings, Ocean Optics XIX, Castelvecchio Pascoli

Assessment of summer rainfall forecast skill in the Intra-Americas in GFDL high and low-resolution models

NASA Astrophysics Data System (ADS)

Krishnamurthy, Lakshmi; Muñoz, Ángel G.; Vecchi, Gabriel A.; Msadek, Rym; Wittenberg, Andrew T.; Stern, Bill; Gudgel, Rich; Zeng, Fanrong

2018-05-01

The Caribbean low-level jet (CLLJ) is an important component of the atmospheric circulation over the Intra-Americas Sea (IAS) which impacts the weather and climate both locally and remotely. It influences the rainfall variability in the Caribbean, Central America, northern South America, the tropical Pacific and the continental Unites States through the transport of moisture. We make use of high-resolution coupled and uncoupled models from the Geophysical Fluid Dynamics Laboratory (GFDL) to investigate the simulation of the CLLJ and its teleconnections and further compare with low-resolution models. The high-resolution coupled model FLOR shows improvements in the simulation of the CLLJ and its teleconnections with rainfall and SST over the IAS compared to the low-resolution coupled model CM2.1. The CLLJ is better represented in uncoupled models (AM2.1 and AM2.5) forced with observed sea-surface temperatures (SSTs), emphasizing the role of SSTs in the simulation of the CLLJ. Further, we determine the forecast skill for observed rainfall using both high- and low-resolution predictions of rainfall and SSTs for the July-August-September season. We determine the role of statistical correction of model biases, coupling and horizontal resolution on the forecast skill. Statistical correction dramatically improves area-averaged forecast skill. But the analysis of spatial distribution in skill indicates that the improvement in skill after statistical correction is region dependent. Forecast skill is sensitive to coupling in parts of the Caribbean, Central and northern South America, and it is mostly insensitive over North America. Comparison of forecast skill between high and low-resolution coupled models does not show any dramatic difference. However, uncoupled models show improvement in the area-averaged skill in the high-resolution atmospheric model compared to lower resolution model. Understanding and improving the forecast skill over the IAS has important implications for highly vulnerable nations in the region.

Verification and Validation of a Navy ESPC Hindcast with Loosely Coupled Data Assimilation

NASA Astrophysics Data System (ADS)

Metzger, E. J.; Barton, N. P.; Smedstad, O. M.; Ruston, B. C.; Wallcraft, A. J.; Whitcomb, T. R.; Ridout, J. A.; Franklin, D. S.; Zamudio, L.; Posey, P. G.; Reynolds, C. A.; Phelps, M.

2016-12-01

The US Navy is developing an Earth System Prediction Capability (ESPC) to provide global environmental information to meet Navy and Department of Defense (DoD) operations and planning needs from the upper atmosphere to under the sea. It will be a fully coupled global atmosphere/ocean/ice/wave/land prediction system providing daily deterministic forecasts out to 16 days at high horizontal and vertical resolution, and daily probabilistic forecasts out to 45 days at lower resolution. The system will run at the Navy DoD Supercomputing Resource Center with an initial operational capability scheduled for the end of FY18 and the final operational capability scheduled for FY22. The individual model and data assimilation components include: atmosphere - NAVy Global Environmental Model (NAVGEM) and Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System - Accelerated Representer (NAVDAS-AR); ocean - HYbrid Coordinate Ocean Model (HYCOM) and Navy Coupled Ocean Data Assimilation (NCODA); ice - Community Ice CodE (CICE) and NCODA; WAVEWATCH III™ and NCODA; and land - NAVGEM Land Surface Model (LSM). Currently, NAVGEM/HYCOM/CICE are three-way coupled and each model component is cycling with its respective assimilation scheme. The assimilation systems do not communicate with each other, but future plans call for these to be coupled as well. NAVGEM runs with a 6-hour update cycle while HYCOM/CICE run with a 24-hour update cycle. The T359L50 NAVGEM/0.08° HYCOM/0.08° CICE system has been integrated in hindcast mode and verification/validation metrics have been computed against unassimilated observations and against stand-alone versions of NAVGEM and HYCOM/CICE. This presentation will focus on typical operational diagnostics for atmosphere, ocean, and ice analyses including 500 hPa atmospheric height anomalies, low-level winds, temperature/salinity ocean depth profiles, ocean acoustical proxies, sea ice edge, and sea ice drift. Overall, the global coupled ESPC system is performing with comparable skill to the stand-alone systems at the nowcast time.

Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions

NASA Astrophysics Data System (ADS)

Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara

2016-06-01

Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash-Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

The impact of underwater glider observations in the forecast of Hurricane Gonzalo (2014)

NASA Astrophysics Data System (ADS)

Goni, G. J.; Domingues, R. M.; Kim, H. S.; Domingues, R. M.; Halliwell, G. R., Jr.; Bringas, F.; Morell, J. M.; Pomales, L.; Baltes, R.

2017-12-01

The tropical Atlantic basin is one of seven global regions where tropical cyclones (TC) are commonly observed to originate and intensify from June to November. On average, approximately 12 TCs travel through the region every year, frequently affecting coastal, and highly populated areas. In an average year, 2 to 3 of them are categorized as intense hurricanes. Given the appropriate atmospheric conditions, TC intensification has been linked to ocean conditions, such as increased ocean heat content and enhanced salinity stratification near the surface. While errors in hurricane track forecasts have been reduced during the last years, errors in intensity forecasts remain mostly unchanged. Several studies have indicated that the use of in situ observations has the potential to improve the representation of the ocean to correctly initialize coupled hurricane intensity forecast models. However, a sustained in situ ocean observing system in the tropical North Atlantic Ocean and Caribbean Sea dedicated to measuring subsurface thermal and salinity fields in support of TC intensity studies and forecasts has yet to be implemented. Autonomous technologies offer new and cost-effective opportunities to accomplish this objective. We highlight here a partnership effort that utilize underwater gliders to better understand air-sea processes during high wind events, and are particularly geared towards improving hurricane intensity forecasts. Results are presented for Hurricane Gonzalo (2014), where glider observations obtained in the tropical Atlantic: Helped to provide an accurate description of the upper ocean conditions, that included the presence of a low salinity barrier layer; Allowed a detailed analysis of the upper ocean response to hurricane force winds of Gonzalo; Improved the initialization of the ocean in a coupled ocean-atmosphere numerical model; and together with observations from other ocean observing platforms, substantially reduced the error in intensity forecast using the HYCOM-HWRF model. Data collected by this project are transmitted in real-time to the Global Telecommunication System, distributed through the institutional web pages, by the IOOS Glider Data Assembly Center, and by NCEI, and assimilated in real-time numerical weather forecast models.

Predicting drifter trajectories and particle dispersion in the Caribbean using a high resolution coastal ocean forecasting system

NASA Astrophysics Data System (ADS)

Solano, M.

2016-02-01

The present study discusses the accuracy of a high-resolution ocean forecasting system in predicting floating drifter trajectories and the uncertainty of modeled particle dispersion in coastal areas. Trajectories were calculated using an offline particle-tracking algorithm coupled to the operational model developed for the region of Puerto Rico by CariCOOS. Both, a simple advection algorithm as well as the Larval TRANSport (LTRANS) model, a more sophisticated offline particle-tracking application, were coupled to the ocean model. Numerical results are compared with 12 floating drifters deployed in the near-shore of Puerto Rico during February and March 2015, and tracked for several days using Global Positioning Systems mounted on the drifters. In addition the trajectories have also been calculated with the AmSeas Navy Coastal Ocean Model (NCOM). The operational model is based on the Regional Ocean Modeling System (ROMS) with a uniform horizontal resolution of 1/100 degrees (1.1km). Initial, surface and open boundary conditions are taken from NCOM, except for wind stress, which is computed using winds from the National Digital Forecasting Database. Probabilistic maps were created to quantify the uncertainty of particle trajectories at different locations. Results show that the forecasted trajectories are location dependent, with tidally active regions having the largest error. The predicted trajectories by both the ROMS and NCOM models show good agreement on average, however both perform differently at particular locations. The effect of wind stress on the drifter trajectories is investigated to account for wind slippage. Furthermore, a real case scenario is presented where simulated trajectories show good agreement when compared to the actual drifter trajectories.

a 24/7 High Resolution Storm Surge, Inundation and Circulation Forecasting System for Florida Coast

NASA Astrophysics Data System (ADS)

Paramygin, V.; Davis, J. R.; Sheng, Y.

2012-12-01

A 24/7 forecasting system for Florida is needed because of the high risk of tropical storm surge-induced coastal inundation and damage, and the need to support operational management of water resources, utility infrastructures, and fishery resources. With the anticipated climate change impacts, including sea level rise, coastal areas are facing the challenges of increasing inundation risk and increasing population. Accurate 24/7 forecasting of water level, inundation, and circulation will significantly enhance the sustainability of coastal communities and environments. Supported by the Southeast Coastal Ocean Observing Regional Association (SECOORA) through NOAA IOOS, a 24/7 high-resolution forecasting system for storm surge, coastal inundation, and baroclinic circulation is being developed for Florida using CH3D Storm Surge Modeling System (CH3D-SSMS). CH3D-SSMS is based on the CH3D hydrodynamic model coupled to a coastal wave model SWAN and basin scale surge and wave models. CH3D-SSMS has been verified with surge, wave, and circulation data from several recent hurricanes in the U.S.: Isabel (2003); Charley, Dennis and Ivan (2004); Katrina and Wilma (2005); Ike and Fay (2008); and Irene (2011), as well as typhoons in the Pacific: Fanapi (2010) and Nanmadol (2011). The effects of tropical cyclones on flow and salinity distribution in estuarine and coastal waters has been simulated for Apalachicola Bay as well as Guana-Tolomato-Matanzas Estuary using CH3D-SSMS. The system successfully reproduced different physical phenomena including large waves during Ivan that damaged I-10 Bridges, a large alongshore wave and coastal flooding during Wilma, salinity drop during Fay, and flooding in Taiwan as a result of combined surge and rain effect during Fanapi. The system uses 4 domains that cover entire Florida coastline: West, which covers the Florida panhandle and Tampa Bay; Southwest spans from Florida Keys to Charlotte Harbor; Southeast, covering Biscayne Bay and Miami and East, which continues north to the Florida/Georgia border. The system has a data acquisition and processing module that is used to collect data for model runs (e.g. wind, river flow, precipitation). Depending on the domain, forecasts runs can take ~1-18 hours to complete on a single CPU (8-core) system (1-2 hrs for 2D setup and up to 18 hrs for a 3D setup) with 4 forecasts generated per day. All data is archived / catalogued and model forecast skill is continuously being evaluated. In addition to the baseline forecasts, additional forecasts are being perform using various options for wind forcing (GFS, GFDL, WRF, and parametric hurricane models), model configurations (2D/ 3D), and open boundary conditions by coupling with large scale models (ROMS, NCOM, HYCOM), as well as incorporating real-time and forecast river flow and precipitation data to better understand how to improve model skill. In addition, new forecast products (e.g. more informative inundation maps) are being developed to targeted stakeholders. To support modern data standards, CH3D-SSMS results are available online via a THREDDS server in CF-Compliant NetCDF format as well as other stakeholder-friendly (e.g. GIS) formats. The SECOORA website provides visualization of the model via GODIVA-THREDDS interface.

Seasonal simulations using a coupled ocean-atmosphere model with data assimilation

NASA Astrophysics Data System (ADS)

Larow, Timothy Edward

1997-10-01

A coupled ocean-atmosphere initialization scheme using Newtonian relaxation has been developed for the Florida State University coupled ocean-atmosphere global general circulation model. The coupled model is used for seasonal predictions of the boreal summers of 1987 and 1988. The atmosphere model is a modified version of the Florida State University global spectral model, resolution triangular truncation 42 waves. The ocean general circulation model consists of a slightly modified version developed by Latif (1987). Coupling is synchronous with exchange of information every two model hours. Using daily analysis from ECMWF and observed monthly mean SSTs from NCEP, two - one year, time dependent, Newtonian relaxation were conducted using the coupled model prior to the seasonal forecasts. Relaxation was selectively applied to the atmospheric vorticity, divergence, temperature, and dew point depression equations, and to the ocean's surface temperature equation. The ocean's initial conditions are from a six year ocean-only simulation which used observed wind stresses and a relaxation towards observed SSTs for forcings. Coupled initialization was conducted from 1 June 1986 to 1 June 1987 for the 1987 boreal forecast and from 1 June 1987 to 1 June 1988 for the 1988 boreal forecast. Examination of annual means of net heat flux, freshwater flux and wind stress obtained by from the initialization show close agreement with Oberhuber (1988) climatology and the Florida State University pseudo wind stress analysis. Sensitivity of the initialization/assimilation scheme was tested by conducting two - ten member ensemble integrations. Each member was integrated for 90 days (June-August) of the respective year. Initial conditions for the ensembles consisted of the same ocean state as used by the initialize forecasts, while the atmospheric initial conditions were from ECMWF analysis centered on 1 June of the respective year. Root mean square error and anomaly correlations between observed and forecasted SSTs in the Nino 3 and Nino 4 regions show greater skill between the initialized forecasts than the ensemble forecasts. It is hypothesized that differences in the specific humidity within the planetary boundary layer are responsible for the large SST errors noted with the ensembles.

An Integrated Uncertainty Analysis and Ensemble-based Data Assimilation Framework for Operational Snow Predictions

NASA Astrophysics Data System (ADS)

He, M.; Hogue, T. S.; Franz, K.; Margulis, S. A.; Vrugt, J. A.

2009-12-01

The National Weather Service (NWS), the agency responsible for short- and long-term streamflow predictions across the nation, primarily applies the SNOW17 model for operational forecasting of snow accumulation and melt. The SNOW17-forecasted snowmelt serves as an input to a rainfall-runoff model for streamflow forecasts in snow-dominated areas. The accuracy of streamflow predictions in these areas largely relies on the accuracy of snowmelt. However, no direct snowmelt measurements are available to validate the SNOW17 predictions. Instead, indirect measurements such as snow water equivalent (SWE) measurements or discharge are typically used to calibrate SNOW17 parameters. In addition, the forecast practice is inherently deterministic, lacking tools to systematically address forecasting uncertainties (e.g., uncertainties in parameters, forcing, SWE and discharge observations, etc.). The current research presents an Integrated Uncertainty analysis and Ensemble-based data Assimilation (IUEA) framework to improve predictions of snowmelt and discharge while simultaneously providing meaningful estimates of the associated uncertainty. The IUEA approach uses the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) to simultaneously estimate uncertainties in model parameters, forcing, and observations. The robustness and usefulness of the IUEA-SNOW17 framework is evaluated for snow-dominated watersheds in the northern Sierra Mountains, using the coupled IUEA-SNOW17 and an operational soil moisture accounting model (SAC-SMA). Preliminary results are promising and indicate successful performance of the coupled IUEA-SNOW17 framework. Implementation of the SNOW17 with the IUEA is straightforward and requires no major modification to the SNOW17 model structure. The IUEA-SNOW17 framework is intended to be modular and transferable and should assist the NWS in advancing the current forecasting system and reinforcing current operational forecasting skill.

Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1) for operational seasonal forecasting

NASA Astrophysics Data System (ADS)

Takaya, Yuhei; Yasuda, Tamaki; Fujii, Yosuke; Matsumoto, Satoshi; Soga, Taizo; Mori, Hirotoshi; Hirai, Masayuki; Ishikawa, Ichiro; Sato, Hitoshi; Shimpo, Akihiko; Kamachi, Masafumi; Ose, Tomoaki

2017-01-01

This paper describes the operational seasonal prediction system of the Japan Meteorological Agency (JMA), the Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1), which was in operation at JMA during the period between February 2010 and May 2015. The predictive skill of the system was assessed with a set of retrospective seasonal predictions (reforecasts) covering 30 years (1981-2010). JMA/MRI-CPS1 showed reasonable predictive skill for the El Niño-Southern Oscillation, comparable to the skills of other state-of-the-art systems. The one-tiered approach adopted in JMA/MRI-CPS1 improved its overall predictive skills for atmospheric predictions over those of the two-tiered approach of the previous uncoupled system. For 3-month predictions with a 1-month lead, JMA/MRI-CPS1 showed statistically significant skills in predicting 500-hPa geopotential height and 2-m temperature in East Asia in most seasons; thus, it is capable of providing skillful seasonal predictions for that region. Furthermore, JMA/MRI-CPS1 was superior overall to the previous system for atmospheric predictions with longer (4-month) lead times. In particular, JMA/MRI-CPS1 was much better able to predict the Asian Summer Monsoon than the previous two-tiered system. This enhanced performance was attributed to the system's ability to represent atmosphere-ocean coupled variability over the Indian Ocean and the western North Pacific from boreal winter to summer following winter El Niño events, which in turn influences the East Asian summer climate through the Pacific-Japan teleconnection pattern. These substantial improvements obtained by using an atmosphere-ocean coupled general circulation model underpin its success in providing more skillful seasonal forecasts on an operational basis.

Evaluation of a regional assimilation system coupled with the WRF-chem model

NASA Astrophysics Data System (ADS)

Liu, Yan-an; Gao, Wei; Huang, Hung-lung; Strabala, Kathleen; Liu, Chaoshun; Shi, Runhe

2013-09-01

Air quality has become a social issue that is causing great concern to humankind across the globe, but particularly in developing countries. Even though the Weather Research and Forecasting with Chemistry (WRF-Chem) model has been applied in many regions, the resolution for inputting meteorology field analysis still impacts the accuracy of forecast. This article describes the application of the CIMSS Regional Assimilation System (CRAS) in East China, and its capability to assimilate the direct broadcast (DB) satellite data for obtaining more detailed meteorological information, including cloud top pressure (CTP) and total precipitation water (TPW) from MODIS. Performance evaluation of CRAS is based on qualitative and quantitative analyses. Compared with data collected from ERA-Interim, Radiosonde, and the Tropical Rainfall Measuring Mission (TRMM) precipitation measurements using bias and Root Mean Square Error (RMSE), CRAS has a systematic error due to the impact of topography and other factors; however, the forecast accuracy of all elements in the model center area is higher at various levels. The bias computed with Radiosonde reveals that the temperature and geopotential height of CRAS are better than ERA-Interim at first guess. Moreover, the location of the 24 h accumulated precipitation forecast are highly consistent with the TRMM retrieval precipitation, which means that the performance of CRAS is excellent. In summation, the newly built Vtable can realize the function of inputting the meteorology field from CRAS output into WRF, which couples the CRAS with WRF-Chem. Therefore, this study not only provides for forecast accuracy of CRAS, but also increases the capability of running the WRF-Chem model at higher resolutions in the future.

Two-stage seasonal streamflow forecasts to guide water resources decisions and water rights allocation

NASA Astrophysics Data System (ADS)

Block, P. J.; Gonzalez, E.; Bonnafous, L.

2011-12-01

Decision-making in water resources is inherently uncertain producing copious risks, ranging from operational (present) to planning (season-ahead) to design/adaptation (decadal) time-scales. These risks include human activity and climate variability/change. As the risks in designing and operating water systems and allocating available supplies vary systematically in time, prospects for predicting and managing such risks become increasingly attractive. Considerable effort has been undertaken to improve seasonal forecast skill and advocate for integration to reduce risk, however only minimal adoption is evident. Impediments are well defined, yet tailoring forecast products and allowing for flexible adoption assist in overcoming some obstacles. The semi-arid Elqui River basin in Chile is contending with increasing levels of water stress and demand coupled with insufficient investment in infrastructure, taxing its ability to meet agriculture, hydropower, and environmental requirements. The basin is fed from a retreating glacier, with allocation principles founded on a system of water rights and markets. A two-stage seasonal streamflow forecast at leads of one and two seasons prescribes the probability of reductions in the value of each water right, allowing water managers to inform their constituents in advance. A tool linking the streamflow forecast to a simple reservoir decision model also allows water managers to select a level of confidence in the forecast information.

Deterministic and Probabilistic Metrics of Surface Air Temperature and Precipitation in the MiKlip Decadal Prediction System

NASA Astrophysics Data System (ADS)

Kadow, Christopher; Illing, Sebastian; Kunst, Oliver; Pohlmann, Holger; Müller, Wolfgang; Cubasch, Ulrich

2014-05-01

Decadal forecasting of climate variability is a growing need for different parts of society, industry and economy. The German initiative MiKlip (www.fona-miklip.de) focuses on the ongoing processes of medium-term climate prediction. The scientific major project funded by the Federal Ministry of Education and Research in Germany (BMBF) develops a forecast system, that aims for reliable predictions on decadal timescales. Using a single earth system model from the Max-Planck institute (MPI-ESM) and moving from the uninitialized runs on to the first initialized 'Coupled Model Intercomparison Project Phase 5' (CMIP5) hindcast experiments identified possibilities and open scientific tasks. The MiKlip decadal prediction system was improved on different aspects through new initialization techniques and datasets of the ocean and atmosphere. To accompany and emphasize such an improvement of a forecast system, a standardized evaluation system designed by the MiKlip sub-project 'Integrated data and evaluation system for decadal scale prediction' (INTEGRATION) analyzes every step of its evolution. This study aims at combining deterministic and probabilistic skill scores of this prediction system from its unitialized state to anomaly and then full-field oceanic initialization. The improved forecast skill in these different decadal hindcast experiments of surface air temperature and precipitation in the Pacific region and the complex area of the North Atlantic illustrate potential sources of skill. A standardized evaluation leads prediction systems depending on development to find its way to produce reliable forecasts. Different aspects of these research dependencies, e.g. ensemble size, resolution, initializations, etc. will be discussed.

Evaluation of the CFSv2 CMIP5 decadal predictions

NASA Astrophysics Data System (ADS)

Bombardi, Rodrigo J.; Zhu, Jieshun; Marx, Lawrence; Huang, Bohua; Chen, Hua; Lu, Jian; Krishnamurthy, Lakshmi; Krishnamurthy, V.; Colfescu, Ioana; Kinter, James L.; Kumar, Arun; Hu, Zeng-Zhen; Moorthi, Shrinivas; Tripp, Patrick; Wu, Xingren; Schneider, Edwin K.

2015-01-01

Retrospective decadal forecasts were undertaken using the Climate Forecast System version 2 (CFSv2) as part of Coupled Model Intercomparison Project 5. Decadal forecasts were performed separately by the National Center for Environmental Prediction (NCEP) and by the Center for Ocean-Land-Atmosphere Studies (COLA), with the centers using two different analyses for the ocean initial conditions the NCEP Climate Forecast System Reanalysis (CFSR) and the NEMOVAR-COMBINE analysis. COLA also examined the sensitivity to the inclusion of forcing by specified volcanic aerosols. Biases in the CFSv2 for both sets of initial conditions include cold midlatitude sea surface temperatures, and rapid melting of sea ice associated with warm polar oceans. Forecasts from the NEMOVAR-COMBINE analysis showed strong weakening of the Atlantic Meridional Overturning Circulation (AMOC), eventually approaching the weaker AMOC associated with CFSR. The decadal forecasts showed high predictive skill over the Indian, the western Pacific, and the Atlantic Oceans and low skill over the central and eastern Pacific. The volcanic forcing shows only small regional differences in predictability of surface temperature at 2m (T2m) in comparison to forecasts without volcanic forcing, especially over the Indian Ocean. An ocean heat content (OHC) budget analysis showed that the OHC has substantial memory, indicating potential for the decadal predictability of T2m; however, the model has a systematic drift in global mean OHC. The results suggest that the reduction of model biases may be the most productive path towards improving the model's decadal forecasts.

Impact of Targeted Ocean Observations for Improving Ocean Model Initialization for Coupled Hurricane Forecasting

NASA Astrophysics Data System (ADS)

Halliwell, G. R.; Srinivasan, A.; Kourafalou, V. H.; Yang, H.; Le Henaff, M.; Atlas, R. M.

2012-12-01

The accuracy of hurricane intensity forecasts produced by coupled forecast models is influenced by errors and biases in SST forecasts produced by the ocean model component and the resulting impact on the enthalpy flux from ocean to atmosphere that powers the storm. Errors and biases in fields used to initialize the ocean model seriously degrade SST forecast accuracy. One strategy for improving ocean model initialization is to design a targeted observing program using airplanes and in-situ devices such as floats and drifters so that assimilation of the additional data substantially reduces errors in the ocean analysis system that provides the initial fields. Given the complexity and expense of obtaining these additional observations, observing system design methods such as OSSEs are attractive for designing efficient observing strategies. A new fraternal-twin ocean OSSE system based on the HYbrid Coordinate Ocean Model (HYCOM) is used to assess the impact of targeted ocean profiles observed by hurricane research aircraft, and also by in-situ float and drifter deployments, on reducing errors in initial ocean fields. A 0.04-degree HYCOM simulation of the Gulf of Mexico is evaluated as the nature run by determining that important ocean circulation features such as the Loop Current and synoptic cyclones and anticyclones are realistically simulated. The data-assimilation system is run on a 0.08-degree HYCOM mesh with substantially different model configuration than the nature run, and it uses a new ENsemble Kalman Filter (ENKF) algorithm optimized for the ocean model's hybrid vertical coordinates. The OSSE system is evaluated and calibrated by first running Observing System Experiments (OSEs) to evaluate existing observing systems, specifically quantifying the impact of assimilating more than one satellite altimeter, and also the impact of assimilating targeted ocean profiles taken by the NOAA WP-3D hurricane research aircraft in the Gulf of Mexico during the Deepwater Horizon oil spill. OSSE evaluation and calibration is then performed by repeating these two OSEs with synthetic observations and comparing the resulting observing system impact to determine if it differs from the OSE results. OSSEs are first run to evaluate different airborne sampling strategies with respect to temporal frequency of flights and the horizontal separation of upper-ocean profiles during each flight. They are then run to assess the impact of releasing multiple floats and gliders. Evaluation strategy focuses on error reduction in fields important for hurricane forecasting such as the structure of ocean currents and eddies, upper ocean heat content distribution, and upper-ocean stratification.

Sea Ice Outlook for September 2015 June Report - NASA Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

Cullather, Richard I.; Keppenne, Christian L.; Marshak, Jelena; Pawson, Steven; Schubert, Siegfried D.; Suarez, Max J.; Vernieres, Guillaume; Zhao, Bin

2015-01-01

The recent decline in perennial sea ice cover in Arctic Ocean is a topic of enormous scientific interest and has relevance to a broad variety of scientific disciplines and human endeavors including biological and physical oceanography, atmospheric circulation, high latitude ecology, the sustainability of indigenous communities, commerce, and resource exploration. A credible seasonal prediction of sea ice extent would be of substantial use to many of the stakeholders in these fields and may also reveal details on the physical processes that result in the current trends in the ice cover. Forecasts are challenging due in part to limitations in the polar observing network, the large variability in the climate system, and an incomplete knowledge of the significant processes. Nevertheless it is a useful to understand the current capabilities of high latitude seasonal forecasting and identify areas where such forecasts may be improved. Since 2008 the Arctic Research Consortium of the United States (ARCUS) has conducted a seasonal forecasting contest in which the average Arctic sea ice extent for the month of September (the month of the annual extent minimum) is predicted from available forecasts in early June, July, and August. The competition is known as the Sea Ice Outlook (SIO) but recently came under the auspices of the Sea Ice Prediction Network (SIPN), and multi-agency funded project to evaluate the SIO. The forecasts are submitted based on modeling, statistical, and heuristic methods. Forecasts of Arctic sea ice extent from the GMAO are derived from seasonal prediction system of the NASA Goddard Earth Observing System model, version 5 (GEOS 5) coupled atmosphere and ocean general circulation model (AOGCM). The projections are made in order to understand the relative skill of the forecasting system and to determine the effects of future improvements to the system. This years prediction is for a September average Arctic ice extent of 5.030.41 million km2.

A gain-loss framework based on ensemble flow forecasts to switch the urban drainage-wastewater system management towards energy optimization during dry periods

NASA Astrophysics Data System (ADS)

Courdent, Vianney; Grum, Morten; Munk-Nielsen, Thomas; Mikkelsen, Peter S.

2017-05-01

Precipitation is the cause of major perturbation to the flow in urban drainage and wastewater systems. Flow forecasts, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimize the operation of integrated urban drainage-wastewater systems (IUDWSs) during both wet and dry weather periods. Numerical weather prediction (NWP) models have significantly improved in recent years, having increased their spatial and temporal resolution. Finer resolution NWP are suitable for urban-catchment-scale applications, providing longer lead time than radar extrapolation. However, forecasts are inevitably uncertain, and fine resolution is especially challenging for NWP. This uncertainty is commonly addressed in meteorology with ensemble prediction systems (EPSs). Handling uncertainty is challenging for decision makers and hence tools are necessary to provide insight on ensemble forecast usage and to support the rationality of decisions (i.e. forecasts are uncertain and therefore errors will be made; decision makers need tools to justify their choices, demonstrating that these choices are beneficial in the long run). This study presents an economic framework to support the decision-making process by providing information on when acting on the forecast is beneficial and how to handle the EPS. The relative economic value (REV) approach associates economic values with the potential outcomes and determines the preferential use of the EPS forecast. The envelope curve of the REV diagram combines the results from each probability forecast to provide the highest relative economic value for a given gain-loss ratio. This approach is traditionally used at larger scales to assess mitigation measures for adverse events (i.e. the actions are taken when events are forecast). The specificity of this study is to optimize the energy consumption in IUDWS during low-flow periods by exploiting the electrical smart grid market (i.e. the actions are taken when no events are forecast). Furthermore, the results demonstrate the benefit of NWP neighbourhood post-processing methods to enhance the forecast skill and increase the range of beneficial uses.

Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

NASA Astrophysics Data System (ADS)

Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

2017-04-01

Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

Evaluation of a new microphysical aerosol module in the ECMWF Integrated Forecasting System

NASA Astrophysics Data System (ADS)

Woodhouse, Matthew; Mann, Graham; Carslaw, Ken; Morcrette, Jean-Jacques; Schulz, Michael; Kinne, Stefan; Boucher, Olivier

2013-04-01

The Monitoring Atmospheric Composition and Climate II (MACC-II) project will provide a system for monitoring and predicting atmospheric composition. As part of the first phase of MACC, the GLOMAP-mode microphysical aerosol scheme (Mann et al., 2010, GMD) was incorporated within the ECMWF Integrated Forecasting System (IFS). The two-moment modal GLOMAP-mode scheme includes new particle formation, condensation, coagulation, cloud-processing, and wet and dry deposition. GLOMAP-mode is already incorporated as a module within the TOMCAT chemistry transport model and within the UK Met Office HadGEM3 general circulation model. The microphysical, process-based GLOMAP-mode scheme allows an improved representation of aerosol size and composition and can simulate aerosol evolution in the troposphere and stratosphere. The new aerosol forecasting and re-analysis system (known as IFS-GLOMAP) will also provide improved boundary conditions for regional air quality forecasts, and will benefit from assimilation of observed aerosol optical depths in near real time. Presented here is an evaluation of the performance of the IFS-GLOMAP system in comparison to in situ aerosol mass and number measurements, and remotely-sensed aerosol optical depth measurements. Future development will provide a fully-coupled chemistry-aerosol scheme, and the capability to resolve nitrate aerosol.

What the diurnal cycle of precipitation tells us about land-atmosphere coupling strength

NASA Astrophysics Data System (ADS)

Ferguson, Craig; Song, Hyojong; Roundy, Joshua

2015-04-01

The key attributes of a coupled forecast model are the coupling strengths between the land-atmosphere and ocean-atmosphere schemes. If a model cannot skillfully capture the diurnal cycle of clouds and precipitation, then it likely cannot be expected to yield accurate long-term climate projections. The seasonal drought forecast skill shortfalls of the U.S. NCEP Coupled Forecast System Version 2 (CFSv2) have been directly linked to its unrealistically strong land-atmosphere coupling strength. Most models can be similarly categorized, which is to say, sensitivity to the land physics (i.e., soil moisture constraints on evapotranspiration) is too strong. In nature, the land signal: noise ratio appears to be at a much lower value. Diagnosing land-atmosphere coupling strength requires at a minimum: surface soil moisture state, surface turbulent heat fluxes, and atmospheric moisture and instability. Full-on diagnosis would entail hacking into the code and inserting a number of tracers. This study addresses the question: What if, given the soil wetness anomaly, model biases in coupling sign and/or strength could be diagnosed from phase shifts in the diurnal precipitation frequency cycle? We use 34-years of output from the North American Regional Reanalysis (NARR) and North American Land Data Assimilation System Phase 2 (NLDAS-2) to investigate the variation in diurnal precipitation frequency cycle between so-called "wet-advantage" and "dry-advantage" coupling regimes over the U.S. southern Great Plains. Wet-advantage occurs when the atmospheric state is closer to the wet adiabatic rate and convection is triggered by a strong increase in the moist static energy from the surface. In contrast, dry-advantage occurs when the atmosphere is drier and the temperature profile is close to the dry adiabatic lapse rate, which favors convection over areas of large boundary layer growth due to high sensible heat fluxes at the surface. We find that there is a significant difference in the phase of the diurnal precipitation frequency between coupling regimes. Specifically, maximum frequency occurs at 1600 LT and 0500 LT for wet- and dry-advantage samples, respectively. For each of these contrasting regimes, we investigate the relative extent to which diurnal phasing may be attributed to local land -- PBL processes versus influences of the Great Plains low-level jet and large-scale atmospheric circulation.

Next Generation Community Based Unified Global Modeling System Development and Operational Implementation Strategies at NCEP

NASA Astrophysics Data System (ADS)

Tallapragada, V.

2017-12-01

NOAA's Next Generation Global Prediction System (NGGPS) has provided the unique opportunity to develop and implement a non-hydrostatic global model based on Geophysical Fluid Dynamics Laboratory (GFDL) Finite Volume Cubed Sphere (FV3) Dynamic Core at National Centers for Environmental Prediction (NCEP), making a leap-step advancement in seamless prediction capabilities across all spatial and temporal scales. Model development efforts are centralized with unified model development in the NOAA Environmental Modeling System (NEMS) infrastructure based on Earth System Modeling Framework (ESMF). A more sophisticated coupling among various earth system components is being enabled within NEMS following National Unified Operational Prediction Capability (NUOPC) standards. The eventual goal of unifying global and regional models will enable operational global models operating at convective resolving scales. Apart from the advanced non-hydrostatic dynamic core and coupling to various earth system components, advanced physics and data assimilation techniques are essential for improved forecast skill. NGGPS is spearheading ambitious physics and data assimilation strategies, concentrating on creation of a Common Community Physics Package (CCPP) and Joint Effort for Data Assimilation Integration (JEDI). Both initiatives are expected to be community developed, with emphasis on research transitioning to operations (R2O). The unified modeling system is being built to support the needs of both operations and research. Different layers of community partners are also established with specific roles/responsibilities for researchers, core development partners, trusted super-users, and operations. Stakeholders are engaged at all stages to help drive the direction of development, resources allocations and prioritization. This talk presents the current and future plans of unified model development at NCEP for weather, sub-seasonal, and seasonal climate prediction applications with special emphasis on implementation of NCEP FV3 Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) into operations by 2019.

Air Quality Forecasting through Different Statistical and Artificial Intelligence Techniques

NASA Astrophysics Data System (ADS)

Mishra, D.; Goyal, P.

2014-12-01

Urban air pollution forecasting has emerged as an acute problem in recent years because there are sever environmental degradation due to increase in harmful air pollutants in the ambient atmosphere. In this study, there are different types of statistical as well as artificial intelligence techniques are used for forecasting and analysis of air pollution over Delhi urban area. These techniques are principle component analysis (PCA), multiple linear regression (MLR) and artificial neural network (ANN) and the forecasting are observed in good agreement with the observed concentrations through Central Pollution Control Board (CPCB) at different locations in Delhi. But such methods suffers from disadvantages like they provide limited accuracy as they are unable to predict the extreme points i.e. the pollution maximum and minimum cut-offs cannot be determined using such approach. Also, such methods are inefficient approach for better output forecasting. But with the advancement in technology and research, an alternative to the above traditional methods has been proposed i.e. the coupling of statistical techniques with artificial Intelligence (AI) can be used for forecasting purposes. The coupling of PCA, ANN and fuzzy logic is used for forecasting of air pollutant over Delhi urban area. The statistical measures e.g., correlation coefficient (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA) of the proposed model are observed in better agreement with the all other models. Hence, the coupling of statistical and artificial intelligence can be use for the forecasting of air pollutant over urban area.

Identifying causes of Western Pacific ITCZ drift in ECMWF System 4 hindcasts

NASA Astrophysics Data System (ADS)

Shonk, Jonathan K. P.; Guilyardi, Eric; Toniazzo, Thomas; Woolnough, Steven J.; Stockdale, Tim

2018-02-01

The development of systematic biases in climate models used in operational seasonal forecasting adversely affects the quality of forecasts they produce. In this study, we examine the initial evolution of systematic biases in the ECMWF System 4 forecast model, and isolate aspects of the model simulations that lead to the development of these biases. We focus on the tendency of the simulated intertropical convergence zone in the western equatorial Pacific to drift northwards by between 0.5° and 3° of latitude depending on season. Comparing observations with both fully coupled atmosphere-ocean hindcasts and atmosphere-only hindcasts (driven by observed sea-surface temperatures), we show that the northward drift is caused by a cooling of the sea-surface temperature on the Equator. The cooling is associated with anomalous easterly wind stress and excessive evaporation during the first twenty days of hindcast, both of which occur whether air-sea interactions are permitted or not. The easterly wind bias develops immediately after initialisation throughout the lower troposphere; a westerly bias develops in the upper troposphere after about 10 days of hindcast. At this point, the baroclinic structure of the wind bias suggests coupling with errors in convective heating, although the initial wind bias is barotropic in structure and appears to have an alternative origin.

A Real-Time California Coastal Ocean Nowcast/Forecast System: Skill Assessment, User Products, and Transition from Research to Operations

NASA Astrophysics Data System (ADS)

Farrara, J. D.; Chao, Y.; Chai, F.; Zhang, H.

2016-02-01

The real-time California coastal ocean nowcast/forecast system is described. The model is based on the Regional Ocean Modeling System (ROMS) and covers the entire California coastal ocean with a horizontal resolution of 3 km and 40 vertical layers. The atmospheric forcing is derived from the operational regional atmospheric model forecasts. The lateral boundary conditions are provided by the operational ocean model forecasts. A multi-scale 3-dimensional variational (3DVAR) data assimilation scheme is used to assimilate both in situ (e.g., vertical profiles of temperature and salinity) and remotely sensed data from both satellite (e.g., sea surface temperature and sea surface height) and land-based platforms (e.g., surface current). The performance of our nowcast/forecast system is evaluated in real-time by a number of metrics that are published as soon as they become available. User tools and products have been developed for both general users and super-users (e.g., NOAA Office of Response and Restoration and USCG). Recent results comparing the 3DVAR with the ensemble Kalman Filter (EnKF) using Data Assimilation Research Testbed (DART) will be presented. Preliminary results coupling the ROMS circulation model with a biogeochemistry/ecosystem model (i.e., CoSiNE) will also discussed. Cloud computing services (e.g., Microsoft, Google) are now being tested to increase the reliability and timeliness in order to be accepted as a truly operational system in the near future.

The added value of stochastic spatial disaggregation for short-term rainfall forecasts currently available in Canada

NASA Astrophysics Data System (ADS)

Gagnon, Patrick; Rousseau, Alain N.; Charron, Dominique; Fortin, Vincent; Audet, René

2017-11-01

Several businesses and industries rely on rainfall forecasts to support their day-to-day operations. To deal with the uncertainty associated with rainfall forecast, some meteorological organisations have developed products, such as ensemble forecasts. However, due to the intensive computational requirements of ensemble forecasts, the spatial resolution remains coarse. For example, Environment and Climate Change Canada's (ECCC) Global Ensemble Prediction System (GEPS) data is freely available on a 1-degree grid (about 100 km), while those of the so-called High Resolution Deterministic Prediction System (HRDPS) are available on a 2.5-km grid (about 40 times finer). Potential users are then left with the option of using either a high-resolution rainfall forecast without uncertainty estimation and/or an ensemble with a spectrum of plausible rainfall values, but at a coarser spatial scale. The objective of this study was to evaluate the added value of coupling the Gibbs Sampling Disaggregation Model (GSDM) with ECCC products to provide accurate, precise and consistent rainfall estimates at a fine spatial resolution (10-km) within a forecast framework (6-h). For 30, 6-h, rainfall events occurring within a 40,000-km2 area (Québec, Canada), results show that, using 100-km aggregated reference rainfall depths as input, statistics of the rainfall fields generated by GSDM were close to those of the 10-km reference field. However, in forecast mode, GSDM outcomes inherit of the ECCC forecast biases, resulting in a poor performance when GEPS data were used as input, mainly due to the inherent rainfall depth distribution of the latter product. Better performance was achieved when the Regional Deterministic Prediction System (RDPS), available on a 10-km grid and aggregated at 100-km, was used as input to GSDM. Nevertheless, most of the analyzed ensemble forecasts were weakly consistent. Some areas of improvement are identified herein.

Ionospheric Data Assimilation and Targeted Observation Strategies: Proof of Concept Analysis in a Geomagnetic Storm Event

NASA Astrophysics Data System (ADS)

Kostelich, Eric; Durazo, Juan; Mahalov, Alex

2017-11-01

The dynamics of the ionosphere involve complex interactions between the atmosphere, solar wind, cosmic radiation, and Earth's magnetic field. Geomagnetic storms arising from solar activity can perturb these dynamics sufficiently to disrupt radio and satellite communications. Efforts to predict ``space weather,'' including ionospheric dynamics, require the development of a data assimilation system that combines observing systems with appropriate forecast models. This talk will outline a proof-of-concept targeted observation strategy, consisting of the Local Ensemble Transform Kalman Filter, coupled with the Thermosphere Ionosphere Electrodynamics Global Circulation Model, to select optimal locations where additional observations can be made to improve short-term ionospheric forecasts. Initial results using data and forecasts from the geomagnetic storm of 26-27 September 2011 will be described. Work supported by the Air Force Office of Scientific Research (Grant Number FA9550-15-1-0096) and by the National Science Foundation (Grant Number DMS-0940314).

Probabilistic hydrological nowcasting using radar based nowcasting techniques and distributed hydrological models: application in the Mediterranean area

NASA Astrophysics Data System (ADS)

Poletti, Maria Laura; Pignone, Flavio; Rebora, Nicola; Silvestro, Francesco

2017-04-01

The exposure of the urban areas to flash-floods is particularly significant to Mediterranean coastal cities, generally densely-inhabited. Severe rainfall events often associated to intense and organized thunderstorms produced, during the last century, flash-floods and landslides causing serious damages to urban areas and in the worst events led to human losses. The temporal scale of these events has been observed strictly linked to the size of the catchments involved: in the Mediterranean area a great number of catchments that pass through coastal cities have a small drainage area (less than 100 km2) and a corresponding hydrologic response timescale in the order of a few hours. A suitable nowcasting chain is essential for the on time forecast of this kind of events. In fact meteorological forecast systems are unable to predict precipitation at the scale of these events, small both at spatial (few km) and temporal (hourly) scales. Nowcasting models, covering the time interval of the following two hours starting from the observation try to extend the predictability limits of the forecasting models in support of real-time flood alert system operations. This work aims to present the use of hydrological models coupled with nowcasting techniques. The nowcasting model PhaSt furnishes an ensemble of equi-probable future precipitation scenarios on time horizons of 1-3 h starting from the most recent radar observations. The coupling of the nowcasting model PhaSt with the hydrological model Continuum allows to forecast the flood with a few hours in advance. In this way it is possible to generate different discharge prediction for the following hours and associated return period maps: these maps can be used as a support in the decisional process for the warning system.

Post-processing Seasonal Precipitation Forecasts via Integrating Climate Indices and the Analog Approach

NASA Astrophysics Data System (ADS)

Liu, Y.; Zhang, Y.; Wood, A.; Lee, H. S.; Wu, L.; Schaake, J. C.

2016-12-01

Seasonal precipitation forecasts are a primary driver for seasonal streamflow prediction that is critical for a range of water resources applications, such as reservoir operations and drought management. However, it is well known that seasonal precipitation forecasts from climate models are often biased and also too coarse in spatial resolution for hydrologic applications. Therefore, post-processing procedures such as downscaling and bias correction are often needed. In this presentation, we discuss results from a recent study that applies a two-step methodology to downscale and correct the ensemble mean precipitation forecasts from the Climate Forecast System (CFS). First, CFS forecasts are downscaled and bias corrected using monthly reforecast analogs: we identify past precipitation forecasts that are similar to the current forecast, and then use the finer-scale observational analysis fields from the corresponding dates to represent the post-processed ensemble forecasts. Second, we construct the posterior distribution of forecast precipitation from the post-processed ensemble by integrating climate indices: a correlation analysis is performed to identify dominant climate indices for the study region, which are then used to weight the analysis analogs selected in the first step using a Bayesian approach. The methodology is applied to the California Nevada River Forecast Center (CNRFC) and the Middle Atlantic River Forecast Center (MARFC) regions for 1982-2015, using the North American Land Data Assimilation System (NLDAS-2) precipitation as the analysis. The results from cross validation show that the post-processed CFS precipitation forecast are considerably more skillful than the raw CFS with the analog approach only. Integrating climate indices can further improve the skill if the number of ensemble members considered is large enough; however, the improvement is generally limited to the first couple of months when compared against climatology. Impacts of various factors such as ensemble size, lead time, and choice of climate indices will also be discussed.

Surface wave effect on the upper ocean in marine forecast

NASA Astrophysics Data System (ADS)

Wang, Guansuo; Qiao, Fangli; Xia, Changshui; Zhao, Chang

2015-04-01

An Operational Coupled Forecast System for the seas off China and adjacent (OCFS-C) is constructed based on the paralleled wave-circulation coupled model, which is tested with comprehensive experiments and operational since November 1st, 2007. The main feature of the system is that the wave-induced mixing is considered in circulation model. Daily analyses and three day forecasts of three-dimensional temperature, salinity, currents and wave height are produced. Coverage is global at 1/2 degreed resolution with nested models up to 1/24 degree resolution in China Sea. Daily remote sensing sea surface temperatures (SST) are taken to relax to an analytical product as hot restarting fields for OCFS-C by the Nudging techniques. Forecasting-data inter-comparisons are performed to measure the effectiveness of OCFS-C in predicting upper-ocean quantities including SST, mixed layer depth (MLD) and subsurface temperature. The variety of performance with lead time and real-time is discussed as well using the daily statistic results for SST between forecast and satellite data. Several buoy observations and many Argo profiles are used for this validation. Except the conventional statistical metrics, non-dimension skill scores (SS) is taken to estimate forecast skill. Model SST comparisons with more one year-long SST time series from 2 buoys given a large SS value (more than 0.90). And skill in predicting the seasonal variability of SST is confirmed. Model subsurface temperature comparisons with that from a lot of Argo profiles indicated that OCFS-C has low skill in predicting subsurface temperatures between 80m and 120m. Inter-comparisons of MLD reveal that MLD from model is shallower than that from Argo profiles by about 12m. QCFS-C is successful and steady in predicting MLD. The daily statistic results for SST between 1-d, 2-d and 3-d forecast and data is adopted to describe variability of Skill in predicting SST with lead time or real time. In a word QCFS-C shows reasonable accuracy over a series of studies designed to test ability to predict upper ocean conditions.

Assessment of the APCC Coupled MME Suite in Predicting the Distinctive Climate Impacts of Two Flavors of ENSO during Boreal Winter

NASA Technical Reports Server (NTRS)

Jeong, Hye-In; Lee, Doo Young; Karumuri, Ashok; Ahn, Joong-Bae; Lee, June-Yi; Luo, Jing-Jia; Schemm, Jae-Kyung E.; Hendon, Harry H.; Braganza, Karl; Ham, Yoo-Geun

2012-01-01

Forecast skill of the APEC Climate Center (APCC) Multi-Model Ensemble (MME) seasonal forecast system in predicting two main types of El Nino-Southern Oscillation (ENSO), namely canonical (or cold tongue) and Modoki ENSO, and their regional climate impacts is assessed for boreal winter. The APCC MME is constructed by simple composite of ensemble forecasts from five independent coupled ocean-atmosphere climate models. Based on a hindcast set targeting boreal winter prediction for the period 19822004, we show that the MME can predict and discern the important differences in the patterns of tropical Pacific sea surface temperature anomaly between the canonical and Modoki ENSO one and four month ahead. Importantly, the four month lead MME beats the persistent forecast. The MME reasonably predicts the distinct impacts of the canonical ENSO, including the strong winter monsoon rainfall over East Asia, the below normal rainfall and above normal temperature over Australia, the anomalously wet conditions across the south and cold conditions over the whole area of USA, and the anomalously dry conditions over South America. However, there are some limitations in capturing its regional impacts, especially, over Australasia and tropical South America at a lead time of one and four months. Nonetheless, forecast skills for rainfall and temperature over East Asia and North America during ENSO Modoki are comparable to or slightly higher than those during canonical ENSO events.

Diagnosing the Nature of Land-Atmosphere Coupling: A Case Study of Dry/Wet Extremes

NASA Technical Reports Server (NTRS)

Santanello, Joseph A., Jr.; Peters-Lidard, Christa; Kennedy, Aaron D.

2012-01-01

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address deficiencies in numerical weather prediction and climate models due to improper treatment of L-A interactions, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land-PBL coupling at the process-level. In this study, a diagnosis of the nature and impacts oflocalland-atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of2006-7 in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to NASA's Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation are examined for the dry/wet regimes of this region, along with the behavior and accuracy of different land-PBL scheme couplings under these conditions. In addition, we examine the impact of improved specification ofland surface states, anomalies, and fluxes that are obtained through the use of a hew optimization and uncertainty module in LIS, on the L-A coupling in WRF forecasts. Results demonstrate how LoCo diagnostics can be applied to coupled model components in the context of their integrated impacts on the process-chain connecting the land surface to the PBL and support of hydrological anomalies.

Arctic sea ice trends, variability and implications for seasonal ice forecasting

PubMed Central

Serreze, Mark C.; Stroeve, Julienne

2015-01-01

September Arctic sea ice extent over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal ice forecasting. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability. PMID:26032315

The CONCEPTS Global Ice-Ocean Prediction System: Establishing an Environmental Prediction Capability in Canada

NASA Astrophysics Data System (ADS)

Pellerin, Pierre; Smith, Gregory; Testut, Charles-Emmanuel; Surcel Colan, Dorina; Roy, Francois; Reszka, Mateusz; Dupont, Frederic; Lemieux, Jean-Francois; Beaudoin, Christiane; He, Zhongjie; Belanger, Jean-Marc; Deacu, Daniel; Lu, Yimin; Buehner, Mark; Davidson, Fraser; Ritchie, Harold; Lu, Youyu; Drevillon, Marie; Tranchant, Benoit; Garric, Gilles

2015-04-01

Here we describe a new system implemented recently at the Canadian Meteorological Centre (CMC) entitled the Global Ice Ocean Prediction System (GIOPS). GIOPS provides ice and ocean analyses and 10 day forecasts daily at 00GMT on a global 1/4° resolution grid. GIOPS includes a full multivariate ocean data assimilation system that combines satellite observations of sea level anomaly and sea surface temperature (SST) together with in situ observations of temperature and salinity. In situ observations are obtained from a variety of sources including: the Argo network of autonomous profiling floats, moorings, ships of opportunity, marine mammals and research cruises. Ocean analyses are blended with sea ice analyses produced by the Global Ice Analysis System.. GIOPS has been developed as part of the Canadian Operational Network of Coupled Environmental PredicTion Systems (CONCEPTS) tri-departmental initiative between Environment Canada, Fisheries and Oceans Canada and National Defense. The development of GIOPS was made through a partnership with Mercator-Océan, a French operational oceanography group. Mercator-Océan provided the ocean data assimilation code and assistance with the system implementation. GIOPS has undergone a rigorous evaluation of the analysis, trial and forecast fields demonstrating its capacity to provide high-quality products in a robust and reliable framework. In particular, SST and ice concentration forecasts demonstrate a clear benefit with respect to persistence. These results support the use of GIOPS products within other CMC operational systems, and more generally, as part of a Government of Canada marine core service. Impact of a two-way coupling between the GEM atmospheric model and NEMO-CICE ocean-ice model will also be presented.

The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model

NASA Astrophysics Data System (ADS)

Baehr, J.; Fröhlich, K.; Botzet, M.; Domeisen, D. I. V.; Kornblueh, L.; Notz, D.; Piontek, R.; Pohlmann, H.; Tietsche, S.; Müller, W. A.

2015-05-01

A seasonal forecast system is presented, based on the global coupled climate model MPI-ESM as used for CMIP5 simulations. We describe the initialisation of the system and analyse its predictive skill for surface temperature. The presented system is initialised in the atmospheric, oceanic, and sea ice component of the model from reanalysis/observations with full field nudging in all three components. For the initialisation of the ensemble, bred vectors with a vertically varying norm are implemented in the ocean component to generate initial perturbations. In a set of ensemble hindcast simulations, starting each May and November between 1982 and 2010, we analyse the predictive skill. Bias-corrected ensemble forecasts for each start date reproduce the observed surface temperature anomalies at 2-4 months lead time, particularly in the tropics. Niño3.4 sea surface temperature anomalies show a small root-mean-square error and predictive skill up to 6 months. Away from the tropics, predictive skill is mostly limited to the ocean, and to regions which are strongly influenced by ENSO teleconnections. In summary, the presented seasonal prediction system based on a coupled climate model shows predictive skill for surface temperature at seasonal time scales comparable to other seasonal prediction systems using different underlying models and initialisation strategies. As the same model underlying our seasonal prediction system—with a different initialisation—is presently also used for decadal predictions, this is an important step towards seamless seasonal-to-decadal climate predictions.

Climate Prediction Center - Outlooks: CFS Forecast of Seasonal Climate

Science.gov Websites

National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site government Web resources and services. CFS Seasonal Climate Forecasts CFS Forecast of Seasonal Climate discontinued after October 2012. This page displays seasonal climate anomalies from the NCEP coupled forecast

Evaluating NMME Seasonal Forecast Skill for use in NASA SERVIR Hub Regions

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Roberts, Franklin R.

2013-01-01

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The coupled forecasts have numerous potential applications, both national and international in scope. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for seasonal forecast use in application modeling (e.g. hydrology, agriculture) is bias correction and skill assessment. Efforts to address systematic biases and multi-model combination in support of NASA SERVIR impact modeling requirements will be highlighted. Specifically, quantilequantile mapping for bias correction has been implemented for all archived NMME hindcasts. Both deterministic and probabilistic skill estimates for raw, bias-corrected, and multi-model ensemble forecasts as a function of forecast lead will be presented for temperature and precipitation. Complementing this statistical assessment will be case studies of significant events, for example, the ability of the NMME forecasts suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.

Crowd Sourcing Approach for UAS Communication Resource Demand Forecasting

NASA Technical Reports Server (NTRS)

Wargo, Chris A.; Difelici, John; Roy, Aloke; Glaneuski, Jason; Kerczewski, Robert J.

2016-01-01

Congressional attention to Unmanned Aircraft Systems (UAS) has caused the Federal Aviation Administration (FAA) to move the National Airspace System (NAS) Integration project forward, but using guidelines, practices and procedures that are yet to be fully integrated with the FAA Aviation Management System. The real drive for change in the NAS will to come from both UAS operators and the government jointly seeing an accurate forecast of UAS usage demand data. This solid forecast information would truly get the attention of planners. This requires not an aggregate demand, but rather a picture of how the demand is spread across small to large UAS, how it is spread across a wide range of missions, how it is expected over time and where, in terms of geospatial locations, will the demand appear. In 2012 the Volpe Center performed a study of the overall future demand for UAS. This was done by aggregate classes of aircraft types. However, the realistic expected demand will appear in clusters of aircraft activities grouped by similar missions on a smaller geographical footprint and then growing from those small cells. In general, there is not a demand forecast that is tightly coupled to the real purpose of the mission requirements (e.g. in terms of real locations and physical structures such as wind mills to inspect, farms to survey, pipelines to patrol, etc.). Being able to present a solid basis for the demand is crucial to getting the attention of investment, government and other fiscal planners. To this end, Mosaic ATM under NASA guidance is developing a crowd sourced, demand forecast engine that can draw forecast details from commercial and government users and vendors. These forecasts will be vetted by a governance panel and then provide for a sharable accurate set of projection data. Our paper describes the project and the technical approach we are using to design and create access for users to the forecast system.

Air quality real-time forecast before and during the G-20 ...

EPA Pesticide Factsheets

The 2016 G-20 Hangzhou summit, the eleventh annual meeting of the G-20 heads of government, will be held during September 3-5, 2016 in Hangzhou, China. For a successful summit, it is important to ensure good air quality. To achieve this goal, governments of Hangzhou and its surrounding provinces will enforce a series of emission reductions, such as a forced closure of major highly-polluting industries and also limiting car and construction emissions in the cities and surroundings during the 2016 G-20 Hangzhou summit. Air quality forecast systems consisting of the two-way coupled WRF-CMAQ and online-coupled WRF-Chem have been applied to forecast air quality in Hangzhou regularly. This study will present the results of real-time forecasts of air quality over eastern China using 12-km grid spacing and for Hangzhou area using 4-km grid spacing with these two modeling systems using emission inventories for base and 2016 G-20 scenarios before and during the 2016 G-20 Hangzhou summit. Evaluations of models’ performance for both cases for PM2.5, PM10, O3, SO2, NO2, CO, air quality index (AQI), and aerosol optical depth (AOD) are carried out by comparing them with observations obtained from satellites, such as MODIS, and surface monitoring networks. The effects of the emission reduction efforts on expected air quality improvements during the2016 G-20 Hangzhou summit will be studied in depth. This study provides insights on how air quality will be improved by a plan

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

NASA Astrophysics Data System (ADS)

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

Impact of Land Model Calibration on Coupled Land-Atmosphere Prediction

NASA Technical Reports Server (NTRS)

Santanello, Joseph A., Jr.; Kumar, Sujay V.; Peters-Lidard, Christa D.; Harrison, Ken; Zhou, Shujia

2012-01-01

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry and wet land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through calibration of the Noah land surface model using the new optimization and uncertainty estimation subsystem in NASA's Land Information System (LIS-OPT/UE). The impact of the calibration on the a) spinup of the land surface used as initial conditions, and b) the simulated heat and moisture states and fluxes of the coupled WRF simulations is then assessed. Changes in ambient weather and land-atmosphere coupling are evaluated along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Results indicate that the offline calibration leads to systematic improvements in land-PBL fluxes and near-surface temperature and humidity, and in the process provide guidance on the questions of what, how, and when to calibrate land surface models for coupled model prediction.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): assessing the added value of probabilistic forecasts

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Fundel, F.; Zappa, M.

2012-04-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on deterministic (COSMO-7) and probabilistic (COSMO-LEPS) atmospheric forecasts, which are used to force a semi-distributed hydrological model (PREVAH) coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which we assessed the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added value conveyed by the probability information, a 31-month reforecast was produced for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain is of up to 2 days lead time for the catchment considered. Brier skill scores show that probabilistic hydrological forecasts outperform their deterministic counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. We finally highlight challenges for making decisions on the basis of hydrological predictions, and discuss the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment.

Simulating surface oil transport during the Deepwater Horizon oil spill: Experiments with the BioCast system

NASA Astrophysics Data System (ADS)

Jolliff, Jason Keith; Smith, Travis A.; Ladner, Sherwin; Arnone, Robert A.

2014-03-01

The U.S. Naval Research Laboratory (NRL) is developing nowcast/forecast software systems designed to combine satellite ocean color data streams with physical circulation models in order to produce prognostic fields of ocean surface materials. The Deepwater Horizon oil spill in the Gulf of Mexico provided a test case for the Bio-Optical Forecasting (BioCast) system to rapidly combine the latest satellite imagery of the oil slick distribution with surface circulation fields in order to produce oil slick transport scenarios and forecasts. In one such sequence of experiments, MODIS satellite true color images were combined with high-resolution ocean circulation forecasts from the Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS®) to produce 96-h oil transport simulations. These oil forecasts predicted a major oil slick landfall at Grand Isle, Louisiana, USA that was subsequently observed. A key driver of the landfall scenario was the development of a coastal buoyancy current associated with Mississippi River Delta freshwater outflow. In another series of experiments, longer-term regional circulation model results were combined with oil slick source/sink scenarios to simulate the observed containment of surface oil within the Gulf of Mexico. Both sets of experiments underscore the importance of identifying and simulating potential hydrodynamic conduits of surface oil transport. The addition of explicit sources and sinks of surface oil concentrations provides a framework for increasingly complex oil spill modeling efforts that extend beyond horizontal trajectory analysis.

Data-Driven Disease Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Generous, Nicholas

If disease outbreaks could be forecasted like the weather, communities could set up protective measures to mitigate their impact. At Los Alamos National Laboratory, scientists are improving disease-forecasting mathematical models by using clinical data--as well as internet data sources such as Wikipedia, Twitter, and Google--and coupling it with satellite imagery. The goal is to better understanding how diseases spread and, eventually, forecast disease outbreaks.

Different types of drifts in two seasonal forecast systems and their dependence on ENSO

NASA Astrophysics Data System (ADS)

Hermanson, L.; Ren, H.-L.; Vellinga, M.; Dunstone, N. D.; Hyder, P.; Ineson, S.; Scaife, A. A.; Smith, D. M.; Thompson, V.; Tian, B.; Williams, K. D.

2017-11-01

Seasonal forecasts using coupled ocean-atmosphere climate models are increasingly employed to provide regional climate predictions. For the quality of forecasts to improve, regional biases in climate models must be diagnosed and reduced. The evolution of biases as initialized forecasts drift away from the observations is poorly understood, making it difficult to diagnose the causes of climate model biases. This study uses two seasonal forecast systems to examine drifts in sea surface temperature (SST) and precipitation, and compares them to the long-term bias in the free-running version of each model. Drifts are considered from daily to multi-annual time scales. We define three types of drift according to their relation with the long-term bias in the free-running model: asymptoting, overshooting and inverse drift. We find that precipitation almost always has an asymptoting drift. SST drifts on the other hand, vary between forecasting systems, where one often overshoots and the other often has an inverse drift. We find that some drifts evolve too slowly to have an impact on seasonal forecasts, even though they are important for climate projections. The bias found over the first few days can be very different from that in the free-running model, so although daily weather predictions can sometimes provide useful information on the causes of climate biases, this is not always the case. We also find that the magnitude of equatorial SST drifts, both in the Pacific and other ocean basins, depends on the El Niño Southern Oscillation (ENSO) phase. Averaging over all hindcast years can therefore hide the details of ENSO state dependent drifts and obscure the underlying physical causes. Our results highlight the need to consider biases across a range of timescales in order to understand their causes and develop improved climate models.

The PRESSCA operational early warning system for landslide forecasting: the 11-12 November 2013 rainfall event in Central Italy.

NASA Astrophysics Data System (ADS)

Ciabatta, Luca; Brocca, Luca; Ponziani, Francesco; Berni, Nicola; Stelluti, Marco; Moramarco, Tommaso

2014-05-01

The Umbria Region, located in Central Italy, is one of the most landslide risk prone area in Italy, almost yearly affected by landslides events at different spatial scales. For early warning procedures aimed at the assessment of the hydrogeological risk, the rainfall thresholds represent the main tool for the Italian Civil Protection System. As shown in previous studies, soil moisture plays a key-role in landslides triggering. In fact, acting on the pore water pressure, soil moisture influences the rainfall amount needed for activating a landslide. In this work, an operational physically-based early warning system, named PRESSCA, that takes into account soil moisture for the definition of rainfall thresholds is presented. Specifically, the soil moisture conditions are evaluated in PRESSCA by using a distributed soil water balance model that is recently coupled with near real-time satellite soil moisture product obtained from ASCAT (Advanced SCATterometer) and from in-situ monitoring data. The integration of three different sources of soil moisture information allows to estimate the most accurate possible soil moisture condition. Then, both observed and forecasted rainfall data are compared with the soil moisture-based thresholds in order to obtain risk indicators over a grid of ~ 5 km. These indicators are then used for the daily hydrogeological risk evaluation and management by the Civil Protection regional service, through the sharing/delivering of near real-time landslide risk scenarios (also through an open source web platform: www.cfumbria.it). On the 11th-12th November, 2013, Umbria Region was hit by an exceptional rainfall event with up to 430mm/72hours that resulted in significant economic damages, but fortunately no casualties among the population. In this study, the results during the rainfall event of PRESSCA system are described, by underlining the model capability to reproduce, two days in advance, landslide risk scenarios in good spatial and temporal agreement with the occurred actual conditions. High-resolution risk scenarios (100mx100m), obtained by coupling PRESSCA forecasts with susceptibility and vulnerability layers, are also produced. The results show good relationship between the PRESSCA forecast and the reported landslides to the Civil Protection Service during the rainfall event, confirming the system robustness. The good forecasts of PRESSCA system have surely contributed to start well in advance the Civil Protection operations (alerting local authorities and population).

The NASA Seasonal-to-Interannual Prediction Project (NSIPP). [Annual Report for 2000

NASA Technical Reports Server (NTRS)

Rienecker, Michele; Suarez, Max; Adamec, David; Koster, Randal; Schubert, Siegfried; Hansen, James; Koblinsky, Chester (Technical Monitor)

2001-01-01

The goal of the project is to develop an assimilation and forecast system based on a coupled atmosphere-ocean-land-surface-sea-ice model capable of using a combination of satellite and in situ data sources to improve the prediction of ENSO and other major S-I signals and their global teleconnections. The objectives of this annual report are to: (1) demonstrate the utility of satellite data, especially surface height surface winds, air-sea fluxes and soil moisture, in a coupled model prediction system; and (2) aid in the design of the observing system for short-term climate prediction by conducting OSSE's and predictability studies.

Ensemble Streamflow Prediction in Korea: Past and Future 5 Years

NASA Astrophysics Data System (ADS)

Jeong, D.; Kim, Y.; Lee, J.

2005-05-01

The Ensemble Streamflow Prediction (ESP) approach was first introduced in 2000 by the Hydrology Research Group (HRG) at Seoul National University as an alternative probabilistic forecasting technique for improving the 'Water Supply Outlook' That is issued every month by the Ministry of Construction and Transportation in Korea. That study motivated the Korea Water Resources Corporation (KOWACO) to establish their seasonal probabilistic forecasting system for the 5 major river basins using the ESP approach. In cooperation with the HRG, the KOWACO developed monthly optimal multi-reservoir operating systems for the Geum river basin in 2004, which coupled the ESP forecasts with an optimization model using sampling stochastic dynamic programming. The user interfaces for both ESP and SSDP have also been designed for the developed computer systems to become more practical. More projects for developing ESP systems to the other 3 major river basins (i.e. the Nakdong, Han and Seomjin river basins) was also completed by the HRG and KOWACO at the end of December 2004. Therefore, the ESP system has become the most important mid- and long-term streamflow forecast technique in Korea. In addition to the practical aspects, resent research experience on ESP has raised some concerns into ways of improving the accuracy of ESP in Korea. Jeong and Kim (2002) performed an error analysis on its resulting probabilistic forecasts and found that the modeling error is dominant in the dry season, while the meteorological error is dominant in the flood season. To address the first issue, Kim et al. (2004) tested various combinations and/or combining techniques and showed that the ESP probabilistic accuracy could be improved considerably during the dry season when the hydrologic models were combined and/or corrected. In addition, an attempt was also made to improve the ESP accuracy for the flood season using climate forecast information. This ongoing project handles three types of climate forecast information: (1) the Monthly Industrial Meteorology Information Magazine (MIMIM) of the Korea Meteorological Administration (2) the Global Data Assimilation Prediction System (GDAPS), and (3) the US National Centers for Environmental Prediction (NCEP). Each of these forecasts is issued in a unique format: (1) MIMIM is a most-probable-event forecast, (2) GDAPS is a single series of deterministic forecasts, and (3) NCEP is an ensemble of deterministic forecasts. Other minor issues include how long the initial conditions influences the ESP accuracy, and how many ESP scenarios are needed to obtain the best accuracy. This presentation also addresses some future research that is needed for ESP in Korea.

Operational Monitoring and Forecasting in Regional Seas: the Aegean Sea example

NASA Astrophysics Data System (ADS)

Nittis, K.; Perivoliotis, L.; Zervakis, V.; Papadopoulos, A.; Tziavos, C.

2003-04-01

The increasing economic activities in the coastal zone and the associated pressure on the marine environment have raised the interest on monitoring systems able to provide supporting information for its effective management and protection. Such an integrated monitoring, forecasting and information system is being developed during the past years in the Aegean Sea. Its main component is the POSEIDON network that provides real-time data for meteorological and surface oceanographic parameters (waves, currents, hydrological and biochemical data) from 11 fixed oceanographic buoys. The numerical forecasting system is composed by an ETA atmospheric model, a WAM wave model and a POM hydrodynamic model that provide every day 72 hours forecasts. The system is operational since May 2000 and its products are published through Internet while a sub-set is also available through cellular telephony. New type of observing platforms will be available in the near future through a number of EU funded research projects. The Mediterranean Moored Multi-sensor Array (M3A) that was developed for the needs of the Mediterranean Forecasting System and was tested during 2000-2001 will be operational in 2004 during the MFSTEP project. The M3A system incorporates sensors for optical and chemical measurements (Oxygen, Turbidity, Chlorophyll-a, Nutrients and PAR) in the euphotic zone (0-100m) together with sensors for physical parameters (Temperature, Salinity, Current speed and direction) at the 0-500m layer. A Ferry-Box system will also operate during 2004 in the southern Aegean Sea, providing surface data for physical and bio-chemical properties. The ongoing modeling efforts include coupling with larger scale circulation models of the Mediterranean, high-resolution downscaling to coastal areas of the Aegean Sea and development of multi-variate data assimilation methods.

CDEP Consortium on Ocean Data Assimilation for Seasonal-to-Interannual Prediction (ODASI)

NASA Technical Reports Server (NTRS)

Rienecker, Michele; Zebiak, Stephen; Kinter, James; Behringer, David; Rosati, Antonio; Kaplan, Alexey

2005-01-01

The ODASI consortium is focused activity of the NOAA/OGP/Climate Diagnostics and Experimental Prediction Program with the goal of improving ocean data assimilation methods and their implementations in support of seasonal forecasts with coupled general circulation models. The consortium is undertaking coordinated assimilation experiments, with common forcing data sets and common input data streams. With different assimilation systems and different models, we aim to understand what approach works best in improving forecast skill in the equatorial Pacific. The presentation will provide an overview of the consortium goals and plans and recent results focused towards evaluating data impacts.

UK Environmental Prediction - integration and evaluation at the convective scale

NASA Astrophysics Data System (ADS)

Lewis, Huw; Brunet, Gilbert; Harris, Chris; Best, Martin; Saulter, Andrew; Holt, Jason; Bricheno, Lucy; Brerton, Ashley; Reynard, Nick; Blyth, Eleanor; Martinez de la Torre, Alberto

2015-04-01

It has long been understood that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. This was well demonstrated in the UK throughout winter 2013/14 when an exceptional run of severe winter storms, often with damaging high winds and intense rainfall led to significant damage from the large waves and storm surge along coastlines, and from saturated soils, high river flows and significant flooding inland. The substantial impacts on individuals, businesses and infrastructure indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, Centre for Ecology & Hydrology and National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus on a 2-year Prototype project will demonstrate the UK coupled prediction concept in research mode, including an analysis of the winter 2013/14 storms and its impacts. By linking science development to operational collaborations such as the UK Natural Hazards Partnership, we can ensure that science priorities are rooted in user requirements. This presentation will provide an overview of UK environmental prediction activities and an update on progress during the first year of the Prototype project. We will present initial results from the coupled model development and discuss the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

HEPEX - achievements and challenges!

NASA Astrophysics Data System (ADS)

Pappenberger, Florian; Ramos, Maria-Helena; Thielen, Jutta; Wood, Andy; Wang, Qj; Duan, Qingyun; Collischonn, Walter; Verkade, Jan; Voisin, Nathalie; Wetterhall, Fredrik; Vuillaume, Jean-Francois Emmanuel; Lucatero Villasenor, Diana; Cloke, Hannah L.; Schaake, John; van Andel, Schalk-Jan

2014-05-01

HEPEX is an international initiative bringing together hydrologists, meteorologists, researchers and end-users to develop advanced probabilistic hydrological forecast techniques for improved flood, drought and water management. HEPEX was launched in 2004 as an independent, cooperative international scientific activity. During the first meeting, the overarching goal was defined as: "to develop and test procedures to produce reliable hydrological ensemble forecasts, and to demonstrate their utility in decision making related to the water, environmental and emergency management sectors." The applications of hydrological ensemble predictions span across large spatio-temporal scales, ranging from short-term and localized predictions to global climate change and regional modeling. Within the HEPEX community, information is shared through its blog (www.hepex.org), meetings, testbeds and intercompaison experiments, as well as project reportings. Key questions of HEPEX are: * What adaptations are required for meteorological ensemble systems to be coupled with hydrological ensemble systems? * How should the existing hydrological ensemble prediction systems be modified to account for all sources of uncertainty within a forecast? * What is the best way for the user community to take advantage of ensemble forecasts and to make better decisions based on them? This year HEPEX celebrates its 10th year anniversary and this poster will present a review of the main operational and research achievements and challenges prepared by Hepex contributors on data assimilation, post-processing of hydrologic predictions, forecast verification, communication and use of probabilistic forecasts in decision-making. Additionally, we will present the most recent activities implemented by Hepex and illustrate how everyone can join the community and participate to the development of new approaches in hydrologic ensemble prediction.

Coupling Recruitment Forecasts with Economics in the Gulf of Maine's American Lobster Fishery

NASA Astrophysics Data System (ADS)

Wahle, R.; Oppenheim, N.; Brady, D. C.; Dayton, A.; Sun, C. H. J.

2016-02-01

Accurate predictions of fishery recruitment and landings represent an important goal of fisheries science and management, but linking environmental drivers of fish population dynamics to financial markets remains a challenge. A fundamental step in that process is understanding the environmental drivers of fishery recruitment. American lobster (Homarus americanus) populations of the northwest Atlantic have been undergoing a dramatic surge, mostly driven by increases the Gulf of Maine. Settler-recruit models that track cohorts after larvae settle to the sea bed are proving useful in predicting subsequent fishery recruitment some 5-7 years later. Here we describe new recruitment forecasting models for the lobster fishery at 11 management areas from Southern New England to Atlantic Canada. We use an annual survey of juvenile year-class strength and environmental indicators to parameterize growth and mortality terms in the model. As a consequence of a recent widespread multi-year downturn in larval settlement, our models suggest that the peak in lobster abundance in the Gulf of Maine will be passed in the near future. We also present initial steps in the coupling of forecast data with economic models for the fishery. We anticipate that these models will give stakeholders and policy makers time to consider their management choices for this most valuable of the region's fisheries. Our vision is to couple our forecast model outputs to an economic model that captures the dynamics of market forces in the New England and Canadian Maritime lobster fisheries. It will then be possible to estimate the financial status of the fishery several years in advance. This early warning system could mitigate the adverse effects of a fluctuating fishery on the coastal communities that are perilously dependent upon it.

Impact of ozone observations on the structure of a tropical cyclone using coupled atmosphere-chemistry data assimilation

NASA Astrophysics Data System (ADS)

Lim, S.; Park, S. K.; Zupanski, M.

2015-04-01

Since the air quality forecast is related to both chemistry and meteorology, the coupled atmosphere-chemistry data assimilation (DA) system is essential to air quality forecasting. Ozone (O3) plays an important role in chemical reactions and is usually assimilated in chemical DA. In tropical cyclones (TCs), O3 usually shows a lower concentration inside the eyewall and an elevated concentration around the eye, impacting atmospheric as well as chemical variables. To identify the impact of O3 observations on TC structure, including atmospheric and chemical information, we employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) with an ensemble-based DA algorithm - the maximum likelihood ensemble filter (MLEF). For a TC case that occurred over the East Asia, our results indicate that the ensemble forecast is reasonable, accompanied with larger background state uncertainty over the TC, and also over eastern China. Similarly, the assimilation of O3 observations impacts atmospheric and chemical variables near the TC and over eastern China. The strongest impact on air quality in the lower troposphere was over China, likely due to the pollution advection. In the vicinity of the TC, however, the strongest impact on chemical variables adjustment was at higher levels. The impact on atmospheric variables was similar in both over China and near the TC. The analysis results are validated using several measures that include the cost function, root-mean-squared error with respect to observations, and degrees of freedom for signal (DFS). All measures indicate a positive impact of DA on the analysis - the cost function and root mean square error have decreased by 16.9 and 8.87%, respectively. In particular, the DFS indicates a strong positive impact of observations in the TC area, with a weaker maximum over northeast China.

Impact of soil moisture initialization on boreal summer subseasonal forecasts: mid-latitude surface air temperature and heat wave events

NASA Astrophysics Data System (ADS)

Seo, Eunkyo; Lee, Myong-In; Jeong, Jee-Hoon; Koster, Randal D.; Schubert, Siegfried D.; Kim, Hye-Mi; Kim, Daehyun; Kang, Hyun-Suk; Kim, Hyun-Kyung; MacLachlan, Craig; Scaife, Adam A.

2018-05-01

This study uses a global land-atmosphere coupled model, the land-atmosphere component of the Global Seasonal Forecast System version 5, to quantify the degree to which soil moisture initialization could potentially enhance boreal summer surface air temperature forecast skill. Two sets of hindcast experiments are performed by prescribing the observed sea surface temperature as the boundary condition for a 15-year period (1996-2010). In one set of the hindcast experiments (noINIT), the initial soil moisture conditions are randomly taken from a long-term simulation. In the other set (INIT), the initial soil moisture conditions are taken from an observation-driven offline Land Surface Model (LSM) simulation. The soil moisture conditions from the offline LSM simulation are calibrated using the forecast model statistics to minimize the inconsistency between the LSM and the land-atmosphere coupled model in their mean and variability. Results show a higher boreal summer surface air temperature prediction skill in INIT than in noINIT, demonstrating the potential benefit from an accurate soil moisture initialization. The forecast skill enhancement appears especially in the areas in which the evaporative fraction—the ratio of surface latent heat flux to net surface incoming radiation—is sensitive to soil moisture amount. These areas lie in the transitional regime between humid and arid climates. Examination of the extreme 2003 European and 2010 Russian heat wave events reveal that the regionally anomalous soil moisture conditions during the events played an important role in maintaining the stationary circulation anomalies, especially those near the surface.

Coupling fast all-season soil strength land surface model with weather research and forecasting model to assess low-level icing in complex terrain

NASA Astrophysics Data System (ADS)

Sines, Taleena R.

Icing poses as a severe hazard to aircraft safety with financial resources and even human lives hanging in the balance when the decision to ground a flight must be made. When analyzing the effects of ice on aviation, a chief cause for danger is the disruption of smooth airflow, which increases the drag force on the aircraft therefore decreasing its ability to create lift. The Weather Research and Forecast (WRF) model Advanced Research WRF (WRF-ARW) is a collaboratively created, flexible model designed to run on distributed computing systems for a variety of applications including forecasting research, parameterization research, and real-time numerical weather prediction. Land-surface models, one of the physics options available in the WRF-ARW, output surface heat and moisture flux given radiation, precipitation, and surface properties such as soil type. The Fast All-Season Soil STrength (FASST) land-surface model was developed by the U.S. Army ERDC-CRREL in Hanover, New Hampshire. Designed to use both meteorological and terrain data, the model calculates heat and moisture within the surface layer as well as the exchange of these parameters between the soil, surface elements (such as snow and vegetation), and atmosphere. Focusing on the Presidential Mountain Range of New Hampshire under the NASA Experimental Program to Stimulate Competitive Research (EPSCoR) Icing Assessments in Cold and Alpine Environments project, one of the main goals is to create a customized, high resolution model to predict and assess ice accretion in complex terrain. The purpose of this research is to couple the FASST land-surface model with the WRF to improve icing forecasts in complex terrain. Coupling FASST with the WRF-ARW may improve icing forecasts because of its sophisticated approach to handling processes such as meltwater, freezing, thawing, and others that would affect the water and energy budget and in turn affect icing forecasts. Several transformations had to take place in order for the FASST land-surface model and WRF-ARW to work together as fully coupled models. Changes had to be made to the WRF-ARW build mechanisms (Chapter 1, section a) so that FASST would be recognized as a new option that could be chosen through the namelist and compiled with other modules. Similarly, FASST had to be altered to no longer read meteorological data from a file, but accept input from WRF-ARW at each time step in a way that did not alter the integrity or run-time processes of the model. Several icing events were available to test the newly coupled model as well as the performance of other available land-surface models from the WRF-ARW. A variation of event intensities and durations from these events were chosen to give a broader view of the land-surface models' abilities to accurately predict icing in complex terrain. Non- icing events were also used in testing to ensure the land-surface models were not predicting ice in the events where none occurred. When compared to the other land-surface models and observations FASST showed a warm bias in several regions. As the forecasts progressed, FASST appeared to attempt to correct this bias and performed similarly to the other land-surface models and at times better than these land-surface models in areas of the domain not affected by this bias. To correct this warm bias, future investigation should be conducted into the reasoning behind this warm bias, including but not limited to: FASST operation and elevation modeling, WRF-ARW variables and forecasting methods, as well as allowing for spin-up prior to forecast times. Following the correction to the warm bias, FASST can be parallelized to allow for operational forecast performance and included in the WRF-ARW forecasting suite for future software releases. (Abstract shortened by UMI.).

Diagnosing the Nature of Land-Atmosphere Coupling During the 2006-7 Dry/Wet Extremes in the U. S. Southern Great Plains

NASA Technical Reports Server (NTRS)

Santanello, Joseph A., Jr.; Peters-Lidard, Christa D.; Kumar, Sujay V.; Dong, Xiquan; Kennedy, Aaron D.

2011-01-01

The degree of coupling between the land surface and PBL in NWP models remains largely undiagnosed due to the complex interactions and feedbacks present across a range of scales. In this study, a framework for diagnosing local land-atmosphere coupling (LoCo) is presented using a coupled mesoscale model with observations during the summers of 2006/7 in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to NASA's Land Information System (LIS), which enables a suite of PBL and land surface model (LSM) options along provides a flexible and high-resolution representation and initialization of land surface physics and states. This coupling is one component of a larger project to develop a NASA-Unified WRF (NU-WRF) system. A range of diagnostics exploring the feedbacks between soil moisture and precipitation are examined for the dry/wet extremes, along with the sensitivity of PBL-LSM coupling to perturbations in soil moisture.

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

NASA Technical Reports Server (NTRS)

Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

2015-01-01

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

NASA Technical Reports Server (NTRS)

Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

2014-01-01

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

How seasonal forecast could help a decision maker: an example of climate service for water resource management

NASA Astrophysics Data System (ADS)

Viel, Christian; Beaulant, Anne-Lise; Soubeyroux, Jean-Michel; Céron, Jean-Pierre

2016-04-01

The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

Seasonal sea ice predictions for the Arctic based on assimilation of remotely sensed observations

NASA Astrophysics Data System (ADS)

Kauker, F.; Kaminski, T.; Ricker, R.; Toudal-Pedersen, L.; Dybkjaer, G.; Melsheimer, C.; Eastwood, S.; Sumata, H.; Karcher, M.; Gerdes, R.

2015-10-01

The recent thinning and shrinking of the Arctic sea ice cover has increased the interest in seasonal sea ice forecasts. Typical tools for such forecasts are numerical models of the coupled ocean sea ice system such as the North Atlantic/Arctic Ocean Sea Ice Model (NAOSIM). The model uses as input the initial state of the system and the atmospheric boundary condition over the forecasting period. This study investigates the potential of remotely sensed ice thickness observations in constraining the initial model state. For this purpose it employs a variational assimilation system around NAOSIM and the Alfred Wegener Institute's CryoSat-2 ice thickness product in conjunction with the University of Bremen's snow depth product and the OSI SAF ice concentration and sea surface temperature products. We investigate the skill of predictions of the summer ice conditions starting in March for three different years. Straightforward assimilation of the above combination of data streams results in slight improvements over some regions (especially in the Beaufort Sea) but degrades the over-all fit to independent observations. A considerable enhancement of forecast skill is demonstrated for a bias correction scheme for the CryoSat-2 ice thickness product that uses a spatially varying scaling factor.

Flood monitoring for ungauged rivers: the power of combining space-based monitoring and global forecasting models

NASA Astrophysics Data System (ADS)

Revilla-Romero, Beatriz; Netgeka, Victor; Raynaud, Damien; Thielen, Jutta

2013-04-01

Flood warning systems typically rely on forecasts from national meteorological services and in-situ observations from hydrological gauging stations. This capacity is not equally developed in flood-prone developing countries. Low-cost satellite monitoring systems and global flood forecasting systems can be an alternative source of information for national flood authorities. The Global Flood Awareness System (GloFAS) has been develop jointly with the European Centre for Medium-Range Weather Forecast (ECMWF) and the Joint Research Centre, and it is running quasi operational now since June 2011. The system couples state-of-the art weather forecasts with a hydrological model driven at a continental scale. The system provides downstream countries with information on upstream river conditions as well as continental and global overviews. In its test phase, this global forecast system provides probabilities for large transnational river flooding at the global scale up to 30 days in advance. It has shown its real-life potential for the first time during the flood in Southeast Asia in 2011, and more recently during the floods in Australia in March 2012, India (Assam, September-October 2012) and Chad Floods (August-October 2012).The Joint Research Centre is working on further research and development, rigorous testing and adaptations of the system to create an operational tool for decision makers, including national and regional water authorities, water resource managers, hydropower companies, civil protection and first line responders, and international humanitarian aid organizations. Currently efforts are being made to link GloFAS to the Global Flood Detection System (GFDS). GFDS is a Space-based river gauging and flood monitoring system using passive microwave remote sensing which was developed by a collaboration between the JRC and Dartmouth Flood Observatory. GFDS provides flood alerts based on daily water surface change measurements from space. Alerts are shown on a world map, with detailed reports for individual gauging sites. A comparison of discharge estimates from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS) with observations for representative climatic zones is presented. Both systems have demonstrated strong potential in forecasting and detecting recent catastrophic floods. The usefulness of their combined information on global scale for decision makers at different levels is discussed. Combining space-based monitoring and global forecasting models is an innovative approach and has significant benefits for international river commissions as well as international aid organisations. This is in line with the objectives of the Hyogo and the Post-2015 Framework that aim at the development of systems which involve trans-boundary collaboration, space-based earth observation, flood forecasting and early warning.

WRF-Fire: coupled weather-wildland fire modeling with the weather research and forecasting model

Treesearch

Janice L. Coen; Marques Cameron; John Michalakes; Edward G. Patton; Philip J. Riggan; Kara M. Yedinak

2012-01-01

A wildland fire behavior module (WRF-Fire) was integrated into the Weather Research and Forecasting (WRF) public domain numerical weather prediction model. The fire module is a surface fire behavior model that is two-way coupled with the atmospheric model. Near-surface winds from the atmospheric model are interpolated to a finer fire grid and used, with fuel properties...

Coupling of wave and circulation models in coastal-ocean predicting systems: A case study for the German Bight

NASA Astrophysics Data System (ADS)

Staneva, Joanna; Wahle, Kathrin

2015-04-01

This study addresses the coupling between wind wave and circulation models on the example of the German Bight and its coastal area called the Wadden Sea (the area between the barrier islands and the coast). This topic reflects the increased interest in operational oceanography to reduce prediction errors of state estimates at coastal scales. The uncertainties in most of the presently used models result from the nonlinear feedback between strong tidal currents and wind-waves, which can no longer be ignored, in particular in the coastal zone where its role seems to be dominant. A nested modelling system is used in the Helmholtz-Zentrum Geesthacht to producing reliable now- and short-term forecasts of ocean state variables, including wind waves and hydrodynamics. In this study we present analysis of wave and hydrographic observations, as well as the results of numerical simulations. The data base includes ADCP observations and continuous measurements from data stations. The individual and collective role of wind, waves and tidal forcing are quantified. The performance of the forecasting system is illustrated for the cases of several extreme events. Effects of ocean waves on coastal circulation and SST simulations are investigated considering wave-dependent stress and wave breaking parameterization during extreme events, e.g. hurricane Xavier in December, 2013. Also the effect which the circulation exerts on the wind waves is tested for the coastal areas using different parameterizations. The improved skill resulting from the new developments in the forecasting system, in particular during extreme events, justifies further enhancements of the coastal pre-operational system for the North Sea and German Bight.

Effect of initial conditions of a catchment on seasonal streamflow prediction using ensemble streamflow prediction (ESP) technique for the Rangitata and Waitaki River basins on the South Island of New Zealand

NASA Astrophysics Data System (ADS)

Singh, Shailesh Kumar; Zammit, Christian; Hreinsson, Einar; Woods, Ross; Clark, Martyn; Hamlet, Alan

2013-04-01

Increased access to water is a key pillar of the New Zealand government plan for economic growths. Variable climatic conditions coupled with market drivers and increased demand on water resource result in critical decision made by water managers based on climate and streamflow forecast. Because many of these decisions have serious economic implications, accurate forecast of climate and streamflow are of paramount importance (eg irrigated agriculture and electricity generation). New Zealand currently does not have a centralized, comprehensive, and state-of-the-art system in place for providing operational seasonal to interannual streamflow forecasts to guide water resources management decisions. As a pilot effort, we implement and evaluate an experimental ensemble streamflow forecasting system for the Waitaki and Rangitata River basins on New Zealand's South Island using a hydrologic simulation model (TopNet) and the familiar ensemble streamflow prediction (ESP) paradigm for estimating forecast uncertainty. To provide a comprehensive database for evaluation of the forecasting system, first a set of retrospective model states simulated by the hydrologic model on the first day of each month were archived from 1972-2009. Then, using the hydrologic simulation model, each of these historical model states was paired with the retrospective temperature and precipitation time series from each historical water year to create a database of retrospective hindcasts. Using the resulting database, the relative importance of initial state variables (such as soil moisture and snowpack) as fundamental drivers of uncertainties in forecasts were evaluated for different seasons and lead times. The analysis indicate that the sensitivity of flow forecast to initial condition uncertainty is depend on the hydrological regime and season of forecast. However initial conditions do not have a large impact on seasonal flow uncertainties for snow dominated catchments. Further analysis indicates that this result is valid when the hindcast database is conditioned by ENSO classification. As a result hydrological forecasts based on ESP technique, where present initial conditions with histological forcing data are used may be plausible for New Zealand catchments.

Coupled hydro-meteorological modelling on a HPC platform for high-resolution extreme weather impact study

NASA Astrophysics Data System (ADS)

Zhu, Dehua; Echendu, Shirley; Xuan, Yunqing; Webster, Mike; Cluckie, Ian

2016-11-01

Impact-focused studies of extreme weather require coupling of accurate simulations of weather and climate systems and impact-measuring hydrological models which themselves demand larger computer resources. In this paper, we present a preliminary analysis of a high-performance computing (HPC)-based hydrological modelling approach, which is aimed at utilizing and maximizing HPC power resources, to support the study on extreme weather impact due to climate change. Here, four case studies are presented through implementation on the HPC Wales platform of the UK mesoscale meteorological Unified Model (UM) with high-resolution simulation suite UKV, alongside a Linux-based hydrological model, Hydrological Predictions for the Environment (HYPE). The results of this study suggest that the coupled hydro-meteorological model was still able to capture the major flood peaks, compared with the conventional gauge- or radar-driving forecast, but with the added value of much extended forecast lead time. The high-resolution rainfall estimation produced by the UKV performs similarly to that of radar rainfall products in the first 2-3 days of tested flood events, but the uncertainties particularly increased as the forecast horizon goes beyond 3 days. This study takes a step forward to identify how the online mode approach can be used, where both numerical weather prediction and the hydrological model are executed, either simultaneously or on the same hardware infrastructures, so that more effective interaction and communication can be achieved and maintained between the models. But the concluding comments are that running the entire system on a reasonably powerful HPC platform does not yet allow for real-time simulations, even without the most complex and demanding data simulation part.

Sol-Terra - AN Operational Space Weather Forecasting Model Framework

NASA Astrophysics Data System (ADS)

Bisi, M. M.; Lawrence, G.; Pidgeon, A.; Reid, S.; Hapgood, M. A.; Bogdanova, Y.; Byrne, J.; Marsh, M. S.; Jackson, D.; Gibbs, M.

2015-12-01

The SOL-TERRA project is a collaboration between RHEA Tech, the Met Office, and RAL Space funded by the UK Space Agency. The goal of the SOL-TERRA project is to produce a Roadmap for a future coupled Sun-to-Earth operational space weather forecasting system covering domains from the Sun down to the magnetosphere-ionosphere-thermosphere and neutral atmosphere. The first stage of SOL-TERRA is underway and involves reviewing current models that could potentially contribute to such a system. Within a given domain, the various space weather models will be assessed how they could contribute to such a coupled system. This will be done both by reviewing peer reviewed papers, and via direct input from the model developers to provide further insight. Once the models have been reviewed then the optimal set of models for use in support of forecast-based SWE modelling will be selected, and a Roadmap for the implementation of an operational forecast-based SWE modelling framework will be prepared. The Roadmap will address the current modelling capability, knowledge gaps and further work required, and also the implementation and maintenance of the overall architecture and environment that the models will operate within. The SOL-TERRA project will engage with external stakeholders in order to ensure independently that the project remains on track to meet its original objectives. A group of key external stakeholders have been invited to provide their domain-specific expertise in reviewing the SOL-TERRA project at critical stages of Roadmap preparation; namely at the Mid-Term Review, and prior to submission of the Final Report. This stakeholder input will ensure that the SOL-TERRA Roadmap will be enhanced directly through the input of modellers and end-users. The overall goal of the SOL-TERRA project is to develop a Roadmap for an operational forecast-based SWE modelling framework with can be implemented within a larger subsequent activity. The SOL-TERRA project is supported within the UK Space Agency's National Space Technology Programme under contract number RP10G0348A03.

Statistical and dynamical forecast of regional precipitation after mature phase of ENSO

NASA Astrophysics Data System (ADS)

Sohn, S.; Min, Y.; Lee, J.; Tam, C.; Ahn, J.

2010-12-01

While the seasonal predictability of general circulation models (GCMs) has been improved, the current model atmosphere in the mid-latitude does not respond correctly to external forcing such as tropical sea surface temperature (SST), particularly over the East Asia and western North Pacific summer monsoon regions. In addition, the time-scale of prediction scope is considerably limited and the model forecast skill still is very poor beyond two weeks. Although recent studies indicate that coupled model based multi-model ensemble (MME) forecasts show the better performance, the long-lead forecasts exceeding 9 months still show a dramatic decrease of the seasonal predictability. This study aims at diagnosing the dynamical MME forecasts comprised of the state of art 1-tier models as well as comparing them with the statistical model forecasts, focusing on the East Asian summer precipitation predictions after mature phase of ENSO. The lagged impact of El Nino as major climate contributor on the summer monsoon in model environments is also evaluated, in the sense of the conditional probabilities. To evaluate the probability forecast skills, the reliability (attributes) diagram and the relative operating characteristics following the recommendations of the World Meteorological Organization (WMO) Standardized Verification System for Long-Range Forecasts are used in this study. The results should shed light on the prediction skill for dynamical model and also for the statistical model, in forecasting the East Asian summer monsoon rainfall with a long-lead time.

Prediction of sea ice thickness cluster in the Northern Hemisphere

NASA Astrophysics Data System (ADS)

Fuckar, Neven-Stjepan; Guemas, Virginie; Johnson, Nathaniel; Doblas-Reyes, Francisco

2016-04-01

Sea ice thickness (SIT) has a potential to contain substantial climate memory and predictability in the northern hemisphere (NH) sea ice system. We use 5-member NH SIT, reconstructed with an ocean-sea-ice general circulation model (NEMOv3.3 with LIM2) with a simple data assimilation routine, to determine NH SIT modes of variability disentangled from the long-term climate change. Specifically, we apply the K-means cluster analysis - one of nonhierarchical clustering methods that partition data into modes or clusters based on their distances in the physical - to determine optimal number of NH SIT clusters (K=3) and their historical variability. To examine prediction skill of NH SIT clusters in EC-Earth2.3, a state-of-the-art coupled climate forecast system, we use 5-member ocean and sea ice initial conditions (IC) from the same ocean-sea-ice historical reconstruction and atmospheric IC from ERA-Interim reanalysis. We focus on May 1st and Nov 1st start dates from 1979 to 2010. Common skill metrics of probability forecast, such as rank probability skill core and ROC (relative operating characteristics - hit rate versus false alarm rate) and reliability diagrams show that our dynamical model predominately perform better than the 1st order Marko chain forecast (that beats climatological forecast) over the first forecast year. On average May 1st start dates initially have lower skill than Nov 1st start dates, but their skill is degraded at slower rate than skill of forecast started on Nov 1st.

Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction

NASA Technical Reports Server (NTRS)

Santanello, Joseph A.; Kumar, Sujay; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Zhou, Shuija

2012-01-01

Land-atmosphere (L-A) Interactions playa critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (US-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF Simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Arctic sea ice trends, variability and implications for seasonal ice forecasting.

PubMed

Serreze, Mark C; Stroeve, Julienne

2015-07-13

September Arctic sea ice extent over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal ice forecasting. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Application, evaluation and sensitivity analysis of the coupled WRF-CMAQ system from regional to urban scales

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science chemical transport model (CTM) capable of simulating the emission, transport and fate of numerous air pollutants. Similarly, the Weather Research and Forecasting (WRF) model is a state-of-the-science mete...

Thermospheric Data Assimilation

DTIC Science & Technology

2016-05-05

forecasting longer than 3 days. Furthermore, validation of assimilation analyses with independent CHAMP mass density observations confirms that the...approach developed in this project. 15. SUBJECT TERMS Data assimilation, Ensemble forecasting , Thermosphere-ionosphere coupled data assimilation...Neutral mass density specification and forecasting , 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 6 19a. NAME

Skill assessment of the coupled physical-biogeochemical operational Mediterranean Forecasting System

NASA Astrophysics Data System (ADS)

Cossarini, Gianpiero; Clementi, Emanuela; Salon, Stefano; Grandi, Alessandro; Bolzon, Giorgio; Solidoro, Cosimo

2016-04-01

The Mediterranean Monitoring and Forecasting Centre (Med-MFC) is one of the regional production centres of the European Marine Environment Monitoring Service (CMEMS-Copernicus). Med-MFC operatively manages a suite of numerical model systems (3DVAR-NEMO-WW3 and 3DVAR-OGSTM-BFM) that provides gridded datasets of physical and biogeochemical variables for the Mediterranean marine environment with a horizontal resolution of about 6.5 km. At the present stage, the operational Med-MFC produces ten-day forecast: daily for physical parameters and bi-weekly for biogeochemical variables. The validation of the coupled model system and the estimate of the accuracy of model products are key issues to ensure reliable information to the users and the downstream services. Product quality activities at Med-MFC consist of two levels of validation and skill analysis procedures. Pre-operational qualification activities focus on testing the improvement of the quality of a new release of the model system and relays on past simulation and historical data. Then, near real time (NRT) validation activities aim at the routinely and on-line skill assessment of the model forecast and relays on the NRT available observations. Med-MFC validation framework uses both independent (i.e. Bio-Argo float data, in-situ mooring and vessel data of oxygen, nutrients and chlorophyll, moored buoys, tide-gauges and ADCP of temperature, salinity, sea level and velocity) and semi-independent data (i.e. data already used for assimilation, such as satellite chlorophyll, Satellite SLA and SST and in situ vertical profiles of temperature and salinity from XBT, Argo and Gliders) We give evidence that different variables (e.g. CMEMS-products) can be validated at different levels (i.e. at the forecast level or at the level of model consistency) and at different spatial and temporal scales. The fundamental physical parameters temperature, salinity and sea level are routinely validated on daily, weekly and quarterly base at regional and sub-regional scale and along specific vertical layers (temperature and salinity); while velocity fields are daily validated against in situ coastal moorings. Since the velocity skill cannot be accurately assessed through coastal measurements due to the actual model horizontal resolution (~6.5 km), new validation metrics and procedures are under investigation. Chlorophyll is the only biogeochemical variable that can be validated routinely at the temporal and spatial scale of the weekly forecast, while nutrients and oxygen predictions can be validated locally or at sub-basin and seasonal scales. For the other biogeochemical variables (i.e. primary production, carbonate system variables) only the accuracy of the average dynamics and model consistency can be evaluated. Then, we discuss the limiting factors of the present validation framework, and the quality and extension of the observing system that would be needed for improving the reliability of the physical and biogeochemical Mediterranean forecast services.

Predictability of a Coupled Model of ENSO Using Singular Vector Analysis: Optimal Growth and Forecast Skill.

NASA Astrophysics Data System (ADS)

Xue, Yan

The optimal growth and its relationship with the forecast skill of the Zebiak and Cane model are studied using a simple statistical model best fit to the original nonlinear model and local linear tangent models about idealized climatic states (the mean background and ENSO cycles in a long model run), and the actual forecast states, including two sets of runs using two different initialization procedures. The seasonally varying Markov model best fit to a suite of 3-year forecasts in a reduced EOF space (18 EOFs) fits the original nonlinear model reasonably well and has comparable or better forecast skill. The initial error growth in a linear evolution operator A is governed by the eigenvalues of A^{T}A, and the square roots of eigenvalues and eigenvectors of A^{T}A are named singular values and singular vectors. One dominant growing singular vector is found, and the optimal 6 month growth rate is largest for a (boreal) spring start and smallest for a fall start. Most of the variation in the optimal growth rate of the two forecasts is seasonal, attributable to the seasonal variations in the mean background, except that in the cold events it is substantially suppressed. It is found that the mean background (zero anomaly) is the most unstable state, and the "forecast IC states" are more unstable than the "coupled model states". One dominant growing singular vector is found, characterized by north-south and east -west dipoles, convergent winds on the equator in the eastern Pacific and a deepened thermocline in the whole equatorial belt. This singular vector is insensitive to initial time and optimization time, but its final pattern is a strong function of initial states. The ENSO system is inherently unpredictable for the dominant singular vector can amplify 5-fold to 24-fold in 6 months and evolve into the large scales characteristic of ENSO. However, the inherent ENSO predictability is only a secondary factor, while the mismatches between the model and data is a primary factor controlling the current forecast skill.

1/32° real-time global ocean prediction and value-added over 1/16° resolution

NASA Astrophysics Data System (ADS)

Shriver, J. F.; Hurlburt, H. E.; Smedstad, O. M.; Wallcraft, A. J.; Rhodes, R. C.

2007-03-01

A 1/32° global ocean nowcast/forecast system has been developed by the Naval Research Laboratory at the Stennis Space Center. It started running at the Naval Oceanographic Office in near real-time on 1 Nov. 2003 and has been running daily in real-time since 1 Mar. 2005. It became an operational system on 6 March 2006, replacing the existing 1/16° system which ceased operation on 12 March 2006. Both systems use the NRL Layered Ocean Model (NLOM) with assimilation of sea surface height from satellite altimeters and sea surface temperature from multi-channel satellite infrared radiometers. Real-time and archived results are available online at http://www.ocean.nrlssc.navy.mil/global_nlom. The 1/32° system has improvements over the earlier system that can be grouped into two categories: (1) better resolution and representation of dynamical processes and (2) design modifications. The design modifications are the result of accrued knowledge since the development of the earlier 1/16° system. The improved horizontal resolution of the 1/32° system has significant dynamical benefits which increase the ability of the model to accurately nowcast and skillfully forecast. At the finer resolution, current pathways and their transports become more accurate, the sea surface height (SSH) variability increases and becomes more realistic and even the global ocean circulation experiences some changes (including inter-basin exchange). These improvements make the 1/32° system a better dynamical interpolator of assimilated satellite altimeter track data, using a one-day model forecast as the first guess. The result is quantitatively more accurate nowcasts, as is illustrated by several model-data comparisons. Based on comparisons with ocean color imagery in the northwestern Arabian Sea and the Gulf of Oman, the 1/32° system has even demonstrated the ability to map small eddies, 25-75 km in diameter, with 70% reliability and a median eddy center location error of 22.5 km, a surprising and unanticipated result from assimilation of altimeter track data. For all of the eddies (50% small eddies), the reliability was 80% and the median eddy center location error was 29 km. The 1/32° system also exhibits improved forecast skill in relation to the 1/16° system. This is due to ( a) a more accurate initial condition for the forecast and ( b) better resolution and representation of critical dynamical processes (such as upper ocean - topographic coupling via mesoscale flow instabilities) which allow the model to more accurately evolve these features in time while running in forecast mode (forecast atmospheric forcing for the first 5 days, then gradually reverting toward climatology for the remainder of the 30-day forecast period). At 1/32° resolution, forecast SSH generally compares better with unassimilated observations and the anomaly correlation of the forecast SSH exceeds that from persistence by a larger amount than found in the 1/16° system.

Development of Operational Wave-Tide-Storm surges Coupling Prediction System

NASA Astrophysics Data System (ADS)

You, S. H.; Park, S. W.; Kim, J. S.; Kim, K. L.

2009-04-01

The Korean Peninsula is surrounded by the Yellow Sea, East China Sea, and East Sea. This complex oceanographic system includes large tides in the Yellow Sea and seasonally varying monsoon and typhoon events. For Korea's coastal regions, floods caused by wave and storm surges are among the most serious threats. To predict more accurate wave and storm surges, the development of coupling wave-tide-storm surges prediction system is essential. For the time being, wave and storm surges predictions are still made separately in KMA (Korea Meteorological Administration) and most operational institute. However, many researchers have emphasized the effects of tides and storm surges on wind waves and recommended further investigations into the effects of wave-tide-storm surges interactions and coupling module. In Korea, especially, tidal height and current give a great effect on the wave prediction in the Yellow sea where is very high tide and related research is not enough. At present, KMA has operated the wave (RWAM : Regional Wave Model) and storm surges/tide prediction system (STORM : Storm Surges/Tide Operational Model) for ocean forecasting. The RWAM is WAVEWATCH III which is a third generation wave model developed by Tolman (1989). The STORM is based on POM (Princeton Ocean Model, Blumberg and Mellor, 1987). The RWAM and STORM cover the northwestern Pacific Ocean from 115°E to 150°E and from 20°N to 52°N. The horizontal grid intervals are 1/12° in both latitudinal and longitudinal directions. These two operational models are coupled to simulate wave heights for typhoon case. The sea level and current simulated by storm surge model are used for the input of wave model with 3 hour interval. The coupling simulation between wave and storm surge model carried out for Typhoon Nabi (0514), Shanshan(0613) and Nari (0711) which were effected on Korea directly. We simulated significant wave height simulated by wave model and coupling model and compared difference between uncoupling and coupling cases for each typhoon. When the typhoon Nabi hit at southern coast of Kyushu, predicted significant wave height reached over 10 m. The difference of significant wave height between wave and wave-tide-storm surges model represents large variation at the southwestern coast of Korea with about 0.5 m. Other typhoon cases also show similar results with typhoon Nabi case. For typhoon Shanshan case the difference of significant wave height reached up to 0.3 m. When the typhoon Nari was affected in the southern coast of Korea, predicted significant wave height was about 5m. The typhoon Nari case also shows the difference of significant wave height similar with other typhoon cases. Using the observation from ocean buoy operated by KMA, we compared wave information simulated by wave and wave-storm surges coupling model. The significant wave height simulated by wave-tide-storm surges model shows the tidal modulation features in the western and southern coast of Korea. And the difference of significant wave height between two models reached up to 0.5 m. The coupling effect also can be identified in the wave direction, wave period and wave length. In addition, wave spectrum is also changeable due to coupling effect of wave-tide-storm surges model. The development, testing and application of a coupling module in which wave-tide-storm surges are incorporated within the frame of KMA Ocean prediction system, has been considered as a step forward in respect of ocean forecasting. In addition, advanced wave prediction model will be applicable to the effect of ocean in the weather forecasting system. The main purpose of this study is to show how the coupling module developed and to report on a series of experiments dealing with the sensitivities and real case prediction of coupling wave-tide-storm surges prediction system.

Integrated Flood Forecast and Virtual Dam Operation System for Water Resources and Flood Risk Management

NASA Astrophysics Data System (ADS)

Shibuo, Yoshihiro; Ikoma, Eiji; Lawford, Peter; Oyanagi, Misa; Kanauchi, Shizu; Koudelova, Petra; Kitsuregawa, Masaru; Koike, Toshio

2014-05-01

While availability of hydrological- and hydrometeorological data shows growing tendency and advanced modeling techniques are emerging, such newly available data and advanced models may not always be applied in the field of decision-making. In this study we present an integrated system of ensemble streamflow forecast (ESP) and virtual dam simulator, which is designed to support river and dam manager's decision making. The system consists of three main functions: real time hydrological model, ESP model, and dam simulator model. In the real time model, the system simulates current condition of river basins, such as soil moisture and river discharges, using LSM coupled distributed hydrological model. The ESP model takes initial condition from the real time model's output and generates ESP, based on numerical weather prediction. The dam simulator model provides virtual dam operation and users can experience impact of dam control on remaining reservoir volume and downstream flood under the anticipated flood forecast. Thus the river and dam managers shall be able to evaluate benefit of priori dam release and flood risk reduction at the same time, on real time basis. Furthermore the system has been developed under the concept of data and models integration, and it is coupled with Data Integration and Analysis System (DIAS) - a Japanese national project for integrating and analyzing massive amount of observational and model data. Therefore it has advantage in direct use of miscellaneous data from point/radar-derived observation, numerical weather prediction output, to satellite imagery stored in data archive. Output of the system is accessible over the web interface, making information available with relative ease, e.g. from ordinary PC to mobile devices. We have been applying the system to the Upper Tone region, located northwest from Tokyo metropolitan area, and we show application example of the system in recent flood events caused by typhoons.

Radar-driven high-resolution hydro-meteorological forecasts of the 26 September 2007 Venice flash flood

NASA Astrophysics Data System (ADS)

Rossa, Andrea M.; Laudanna Del Guerra, Franco; Borga, Marco; Zanon, Francesco; Settin, Tommaso; Leuenberger, Daniel

2010-11-01

SummaryThis study aims to assess the feasibility of assimilating carefully checked radar rainfall estimates into a numerical weather prediction (NWP) to extend the forecasting lead time for an extreme flash flood. The hydro-meteorological modeling chain includes the convection-permitting NWP model COSMO-2 and a coupled hydrological-hydraulic model. Radar rainfall estimates are assimilated into the NWP model via the latent heat nudging method. The study is focused on 26 September 2007 extreme flash flood which impacted the coastal area of North-eastern Italy around Venice. The hydro-meteorological modeling system is implemented over the 90 km2 Dese river basin draining to the Venice Lagoon. The radar rainfall observations are carefully checked for artifacts, including rain-induced signal attenuation, by means of physics-based correction procedures and comparison with a dense network of raingauges. The impact of the radar rainfall estimates in the assimilation cycle of the NWP model is very significant. The main individual organized convective systems are successfully introduced into the model state, both in terms of timing and localization. Also, high-intensity incorrectly localized precipitation is correctly reduced to about the observed levels. On the other hand, the highest rainfall intensities computed after assimilation underestimate the observed values by 20% and 50% at a scale of 20 km and 5 km, respectively. The positive impact of assimilating radar rainfall estimates is carried over into the free forecast for about 2-5 h, depending on when the forecast was started. The positive impact is larger when the main mesoscale convective system is present in the initial conditions. The improvements in the precipitation forecasts are propagated to the river flow simulations, with an extension of the forecasting lead time up to 3 h.

Impacts of calibration strategies and ensemble methods on ensemble flood forecasting over Lanjiang basin, Southeast China

NASA Astrophysics Data System (ADS)

Liu, Li; Xu, Yue-Ping

2017-04-01

Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.

Skill assessment of a real-time forecast system utilizing a coupled hydrologic and coastal hydrodynamic model during Hurricane Irene (2011)

NASA Astrophysics Data System (ADS)

Dresback, Kendra M.; Fleming, Jason G.; Blanton, Brian O.; Kaiser, Carola; Gourley, Jonathan J.; Tromble, Evan M.; Luettich, Richard A.; Kolar, Randall L.; Hong, Yang; Van Cooten, Suzanne; Vergara, Humberto J.; Flamig, Zac L.; Lander, Howard M.; Kelleher, Kevin E.; Nemunaitis-Monroe, Kodi L.

2013-12-01

Due to the devastating effects of recent hurricanes in the Gulf of Mexico (e.g., Katrina, Rita, Ike and Gustav), the development of a high-resolution, real-time, total water level prototype system has been accelerated. The fully coupled model system that includes hydrology is an extension of the ADCIRC Surge Guidance System (ASGS), and will henceforth be referred to as ASGS-STORM (Scalable, Terrestrial, Ocean, River, Meteorological) to emphasize the major processes that are represented by the system.The ASGS-STORM system incorporates tides, waves, winds, rivers and surge to produce a total water level, which provides a holistic representation of coastal flooding. ASGS-STORM was rigorously tested during Hurricane Irene, which made landfall in late August 2011 in North Carolina. All results from ASGS-STORM for the advisories were produced in real-time, forced by forecast wind and pressure fields computed using a parametric tropical cyclone model, and made available via the web. Herein, a skill assessment, analyzing wind speed and direction, significant wave heights, and total water levels, is used to evaluate ASGS-STORM's performance during Irene for three advisories and the best track from the National Hurricane Center (NHC). ASGS-STORM showed slight over-prediction for two advisories (Advisory 23 and 25) due to the over-estimation of the storm intensity. However, ASGS-STORM shows notable skill in capturing total water levels, wind speed and direction, and significant wave heights in North Carolina when utilizing Advisory 28, which had a slight shift in the track but provided a more accurate estimation of the storm intensity, along with the best track from the NHC. Results from ASGS-STORM have shown that as the forecast of the advisories improves, so does the accuracy of the models used in the study; therefore, accurate input from the weather forecast is a necessary, but not sufficient, condition to ensure the accuracy of the guidance provided by the system. While Irene provided a real-time test of the viability of a total water level system, the relatively insignificant freshwater discharges precludes definitive conclusions about the role of freshwater discharges on total water levels in estuarine zones. Now that the system has been developed, on-going work will examine storms (e.g., Floyd) for which the freshwater discharge played a more meaningful role.

Seamless hydrological predictions for a monsoon driven catchment in North-East India

NASA Astrophysics Data System (ADS)

Köhn, Lisei; Bürger, Gerd; Bronstert, Axel

2016-04-01

Improving hydrological forecasting systems on different time scales is interesting and challenging with regards to humanitarian as well as scientific aspects. In meteorological research, short-, medium-, and long-term forecasts are now being merged to form a system of seamless weather and climate predictions. Coupling of these meteorological forecasts with a hydrological model leads to seamless predictions of streamflow, ranging from one day to a season. While there are big efforts made to analyse the uncertainties of probabilistic streamflow forecasts, knowledge of the single uncertainty contributions from meteorological and hydrological modeling is still limited. The overarching goal of this project is to gain knowledge in this subject by decomposing and quantifying the overall predictive uncertainty into its single factors for the entire seamless forecast horizon. Our study area is the Mahanadi River Basin in North-East India, which is prone to severe floods and droughts. Improved streamflow forecasts on different time scales would contribute to early flood warning as well as better water management operations in the agricultural sector. Because of strong inter-annual monsoon variations in this region, which are, unlike the mid-latitudes, partly predictable from long-term atmospheric-oceanic oscillations, the Mahanadi catchment represents an ideal study site. Regionalized precipitation forecasts are obtained by applying the method of expanded downscaling to the ensemble prediction systems of ECMWF and NCEP. The semi-distributed hydrological model HYPSO-RR, which was developed in the Eco-Hydrological Simulation Environment ECHSE, is set up for several sub-catchments of the Mahanadi River Basin. The model is calibrated automatically using the Dynamically Dimensioned Search algorithm, with a modified Nash-Sutcliff efficiency as objective function. Meteorological uncertainty is estimated from the existing ensemble simulations, while the hydrological uncertainty is derived from a statistical post-processor. After running the hydrological model with the precipitation forecasts and applying the hydrological post-processor, the predictive uncertainty of the streamflow forecast can be analysed. The decomposition of total uncertainty is done using a two-way analysis of variance. In this contribution we present the model set-up and the first results of our hydrological forecasts with up to a 180 days lead time, which are derived by using 15 downscaled members of the ECMWF multi-model seasonal forecast ensemble as model input.

Global operational hydrological forecasts through eWaterCycle

NASA Astrophysics Data System (ADS)

van de Giesen, Nick; Bierkens, Marc; Donchyts, Gennadii; Drost, Niels; Hut, Rolf; Sutanudjaja, Edwin

2015-04-01

Central goal of the eWaterCycle project (www.ewatercycle.org) is the development of an operational hyper-resolution hydrological global model. This model is able to produce 14 day ensemble forecasts based on a hydrological model and operational weather data (presently NOAA's Global Ensemble Forecast System). Special attention is paid to prediction of situations in which water related issues are relevant, such as floods, droughts, navigation, hydropower generation, and irrigation stress. Near-real time satellite data will be assimilated in the hydrological simulations, which is a feature that will be presented for the first time at EGU 2015. First, we address challenges that are mainly computer science oriented but have direct practical hydrological implications. An important feature in this is the use of existing standards and open-source software to the maximum extent possible. For example, we use the Community Surface Dynamics Modeling System (CSDMS) approach to coupling models (Basic Model Interface (BMI)). The hydrological model underlying the project is PCR-GLOBWB, built by Utrecht University. This is the motor behind the predictions and state estimations. Parts of PCR-GLOBWB have been re-engineered to facilitate running it in a High Performance Computing (HPC) environment, run parallel on multiple nodes, as well as to use BMI. Hydrological models are not very CPU intensive compared to, say, atmospheric models. They are, however, memory hungry due to the localized processes and associated effective parameters. To accommodate this memory need, especially in an ensemble setting, a variation on the traditional Ensemble Kalman Filter was developed that needs much less on-chip memory. Due to the operational nature, the coupling of the hydrological model with hydraulic models is very important. The idea is not to run detailed hydraulic routing schemes over the complete globe but to have on-demand simulation prepared off-line with respect to topography and parameterizations. This allows for very detailed simulations at hectare to meter scales, where and when this is needed. At EGU 2015, the operational global eWaterCycle model will be presented for the first time, including forecasts at high resolution, the innovative data assimilation approach, and on-demand coupling with hydraulic models.

Fitting and forecasting coupled dark energy in the non-linear regime

DOE Office of Scientific and Technical Information (OSTI.GOV)

Casas, Santiago; Amendola, Luca; Pettorino, Valeria

2016-01-01

We consider cosmological models in which dark matter feels a fifth force mediated by the dark energy scalar field, also known as coupled dark energy. Our interest resides in estimating forecasts for future surveys like Euclid when we take into account non-linear effects, relying on new fitting functions that reproduce the non-linear matter power spectrum obtained from N-body simulations. We obtain fitting functions for models in which the dark matter-dark energy coupling is constant. Their validity is demonstrated for all available simulations in the redshift range 0z=–1.6 and wave modes below 0k=1 h/Mpc. These fitting formulas can be used tomore » test the predictions of the model in the non-linear regime without the need for additional computing-intensive N-body simulations. We then use these fitting functions to perform forecasts on the constraining power that future galaxy-redshift surveys like Euclid will have on the coupling parameter, using the Fisher matrix method for galaxy clustering (GC) and weak lensing (WL). We find that by using information in the non-linear power spectrum, and combining the GC and WL probes, we can constrain the dark matter-dark energy coupling constant squared, β{sup 2}, with precision smaller than 4% and all other cosmological parameters better than 1%, which is a considerable improvement of more than an order of magnitude compared to corresponding linear power spectrum forecasts with the same survey specifications.« less

Using JPSS VIIRS Fire Radiative Power Data to Forecast Biomass Burning Emissions and Smoke Transport by the High Resolution Rapid Refresh Model

NASA Astrophysics Data System (ADS)

Ahmadov, R.; Grell, G. A.; James, E.; Alexander, C.; Stewart, J.; Benjamin, S.; McKeen, S. A.; Csiszar, I. A.; Tsidulko, M.; Pierce, R. B.; Pereira, G.; Freitas, S. R.; Goldberg, M.

2017-12-01

We present a new real-time smoke modeling system, the High Resolution Rapid Refresh coupled with smoke (HRRR-Smoke), to simulate biomass burning (BB) emissions, plume rise and smoke transport in real time. The HRRR is the NOAA Earth System Research Laboratory's 3km grid spacing version of the Weather Research and Forecasting (WRF) model used for weather forecasting. Here we make use of WRF-Chem (the WRF model coupled with chemistry) and simulate fine particulate matter (smoke) emissions emitted by BB. The HRRR-Smoke modeling system ingests fire radiative power (FRP) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (S-NPP) satellite to calculate BB emissions. The FRP product is based on processing 750m resolution "M" bands. The algorithms for fire detection and FRP retrieval are consistent with those used to generate the MODIS fire detection data. For the purpose of ingesting VIIRS fire data into the HRRR-Smoke model, text files are generated to provide the location and detection confidence of fire pixels, as well as FRP. The VIIRS FRP data from the text files are processed and remapped over the HRRR-Smoke model domains. We process the FRP data to calculate BB emissions (smoldering part) and fire size for the model input. In addition, HRRR-Smoke uses the FRP data to simulate the injection height for the flaming emissions using concurrently simulated meteorological fields by the model. Currently, there are two 3km resolution domains covering the contiguous US and Alaska which are used to simulate smoke in real time. In our presentation, we focus on the CONUS domain. HRRR-Smoke is initialized 4 times per day to forecast smoke concentrations for the next 36 hours. The VIIRS FRP data, as well as near-surface and vertically integrated smoke mass concentrations are visualized for every forecast hour. These plots are provided to the public via the HRRR-Smoke web-page: https://rapidrefresh.noaa.gov/HRRRsmoke/. Model evaluations for a case study are presented, where simulated smoke concentrations are compared with hourly PM2.5 measurements from EPA's Air Quality System network. These comparisons demonstrate the model's ability in simulating high aerosol loadings during major wildfire events in the western US.

CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system

NASA Astrophysics Data System (ADS)

Zhang, Hongbo; Singh, Vijay P.; Wang, Bin; Yu, Yinghao

2016-09-01

Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition-prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD-RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD-RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD-based forecasting, and results showed that removing high-frequency component is an effective measure to improve forecasting precision and is suggested for use with the CEREF model for better performance. Finally, the study concluded that the CEREF model can be used to forecast non-stationary annual streamflow change as a co-evolution of hydrologic and social systems with better accuracy. Also, the modification about removing high-frequency can further improve the performance of the CEREF model. It should be noted that the CEREF model is beneficial for data-driven hydrologic forecasting in complex socio-hydrologic systems, and as a simple data-driven socio-hydrologic forecasting model, deserves more attention.

A Study on Mutil-Scale Background Error Covariances in 3D-Var Data Assimilation

NASA Astrophysics Data System (ADS)

Zhang, Xubin; Tan, Zhe-Min

2017-04-01

The construction of background error covariances is a key component of three-dimensional variational data assimilation. There are different scale background errors and interactions among them in the numerical weather Prediction. However, the influence of these errors and their interactions cannot be represented in the background error covariances statistics when estimated by the leading methods. So, it is necessary to construct background error covariances influenced by multi-scale interactions among errors. With the NMC method, this article firstly estimates the background error covariances at given model-resolution scales. And then the information of errors whose scales are larger and smaller than the given ones is introduced respectively, using different nesting techniques, to estimate the corresponding covariances. The comparisons of three background error covariances statistics influenced by information of errors at different scales reveal that, the background error variances enhance particularly at large scales and higher levels when introducing the information of larger-scale errors by the lateral boundary condition provided by a lower-resolution model. On the other hand, the variances reduce at medium scales at the higher levels, while those show slight improvement at lower levels in the nested domain, especially at medium and small scales, when introducing the information of smaller-scale errors by nesting a higher-resolution model. In addition, the introduction of information of larger- (smaller-) scale errors leads to larger (smaller) horizontal and vertical correlation scales of background errors. Considering the multivariate correlations, the Ekman coupling increases (decreases) with the information of larger- (smaller-) scale errors included, whereas the geostrophic coupling in free atmosphere weakens in both situations. The three covariances obtained in above work are used in a data assimilation and model forecast system respectively, and then the analysis-forecast cycles for a period of 1 month are conducted. Through the comparison of both analyses and forecasts from this system, it is found that the trends for variation in analysis increments with information of different scale errors introduced are consistent with those for variation in variances and correlations of background errors. In particular, introduction of smaller-scale errors leads to larger amplitude of analysis increments for winds at medium scales at the height of both high- and low- level jet. And analysis increments for both temperature and humidity are greater at the corresponding scales at middle and upper levels under this circumstance. These analysis increments improve the intensity of jet-convection system which includes jets at different levels and coupling between them associated with latent heat release, and these changes in analyses contribute to the better forecasts for winds and temperature in the corresponding areas. When smaller-scale errors are included, analysis increments for humidity enhance significantly at large scales at lower levels to moisten southern analyses. This humidification devotes to correcting dry bias there and eventually improves forecast skill of humidity. Moreover, inclusion of larger- (smaller-) scale errors is beneficial for forecast quality of heavy (light) precipitation at large (small) scales due to the amplification (diminution) of intensity and area in precipitation forecasts but tends to overestimate (underestimate) light (heavy) precipitation .

Evaluating the improvements of the BOLAM meteorological model operational at ISPRA: A case study approach - preliminary results

NASA Astrophysics Data System (ADS)

Mariani, S.; Casaioli, M.; Lastoria, B.; Accadia, C.; Flavoni, S.

2009-04-01

The Institute for Environmental Protection and Research - ISPRA (former Agency for Environmental Protection and Technical Services - APAT) runs operationally since 2000 an integrated meteo-marine forecasting chain, named the Hydro-Meteo-Marine Forecasting System (Sistema Idro-Meteo-Mare - SIMM), formed by a cascade of four numerical models, telescoping from the Mediterranean basin to the Venice Lagoon, and initialized by means of analyses and forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). The operational integrated system consists of a meteorological model, the parallel verision of BOlogna Limited Area Model (BOLAM), coupled over the Mediterranean sea with a WAve Model (WAM), a high-resolution shallow-water model of the Adriatic and Ionian Sea, namely the Princeton Ocean Model (POM), and a finite-element version of the same model (VL-FEM) on the Venice Lagoon, aimed to forecast the acqua alta events. Recently, the physically based, fully distributed, rainfall-runoff TOPographic Kinematic APproximation and Integration (TOPKAPI) model has been integrated into the system, coupled to BOLAM, over two river basins, located in the central and northeastern part of Italy, respectively. However, at the present time, this latter part of the forecasting chain is not operational and it is used in a research configuration. BOLAM was originally implemented in 2000 onto the Quadrics parallel supercomputer (and for this reason referred to as QBOLAM, as well) and only at the end of 2006 it was ported (together with the other operational marine models of the forecasting chain) onto the Silicon Graphics Inc. (SGI) Altix 8-processor machine. In particular, due to the Quadrics implementation, the Kuo scheme was formerly implemented into QBOLAM for the cumulus convection parameterization. On the contrary, when porting SIMM onto the Altix Linux cluster, it was achievable to implement into QBOLAM the more advanced convection parameterization by Kain and Fritsch. A fully updated serial version of the BOLAM code has been recently acquired. Code improvements include a more precise advection scheme (Weighted Average Flux); explicit advection of five hydrometeors, and state-of-the-art parameterization schemes for radiation, convection, boundary layer turbulence and soil processes (also with possible choice among different available schemes). The operational implementation of the new code into the SIMM model chain, which requires the development of a parallel version, will be achieved during 2009. In view of this goal, the comparative verification of the different model versions' skill represents a fundamental task. On this purpose, it has been decided to evaluate the performance improvement of the new BOLAM code (in the available serial version, hereinafter BOLAM 2007) with respect to the version with the Kain-Fritsch scheme (hereinafter KF version) and to the older one employing the Kuo scheme (hereinafter Kuo version). In the present work, verification of precipitation forecasts from the three BOLAM versions is carried on in a case study approach. The intense rainfall episode occurred on 10th - 17th December 2008 over Italy has been considered. This event produced indeed severe damages in Rome and its surrounding areas. Objective and subjective verification methods have been employed in order to evaluate model performance against an observational dataset including rain gauge observations and satellite imagery. Subjective comparison of observed and forecast precipitation fields is suitable to give an overall description of the forecast quality. Spatial errors (e.g., shifting and pattern errors) and rainfall volume error can be assessed quantitatively by means of object-oriented methods. By comparing satellite images with model forecast fields, it is possible to investigate the differences between the evolution of the observed weather system and the predicted ones, and its sensitivity to the improvements in the model code. Finally, the error in forecasting the cyclone evolution can be tentatively related with the precipitation forecast error.

Forecasting and Communicating Water-Related Disasters in Africa

NASA Astrophysics Data System (ADS)

Hong, Y.; Clark, R. A.; Mandl, D.; Gourley, J. J.; Flamig, Z.; Zhang, K.; Macharia, D.; Frye, S. W.; Cappelaere, P. G.; Handy, M.

2016-12-01

Accurate forecasting and communication of water and water-related hazards in developing regions could save untold lives and property. To this end, the CREST (Coupled Routing and Excess Storage) hydrologic model has been implemented over East Africa, and in dozens of other countries as a user-friendly, flexible, and highly extensible platform for monitoring water resources, floods, droughts, and landslides since 2009. We will present the updated CREST/EF5 hydrologic ensemble modeling framework with new model physics and better forecasts of streamflow, soil moisture, and other hydrologic states to RCMRD (the Regional Centre for Mapping of Resources for Development) and SERVIR global hub network. The central goal of this project is to develop an ensemble hydrologic prediction system, forced by weather and climate forecasts in a single continuum, to communicate forecasts on scales ranging from sub-daily to seasonal and in formats designed for better decision making about water and water-related disasters. The CREST/EF5 is a proven performer at getting researcher and officials in emerging regions excited about and confident in their ability to independently monitor, forecast, and understand water and water-related disasters, through a series of training workshops and capacity building activities in USA, Africa, Mesoamerica, and South Asia and is thus particularly well-suited for hydrologic capacity building in emerging countries.

OpenDA-WFLOW framework for improving hydrologic predictions using distributed hydrologic models

NASA Astrophysics Data System (ADS)

Weerts, Albrecht; Schellekens, Jaap; Kockx, Arno; Hummel, Stef

2017-04-01

Data assimilation (DA) holds considerable potential for improving hydrologic predictions (Liu et al., 2012) and increase the potential for early warning and/or smart water management. However, advances in hydrologic DA research have not yet been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. The objective of this work is to highlight the development of a generic linkage of the open source OpenDA package and the open source community hydrologic modeling framework Openstreams/WFLOW and its application in operational hydrological forecasting on various spatial scales. The coupling between OpenDA and Openstreams/wflow framework is based on the emerging standard Basic Model Interface (BMI) as advocated by CSDMS using cross-platform webservices (i.e. Apache Thrift) developed by Hut et al. (2016). The potential application of the OpenDA-WFLOW for operational hydrologic forecasting including its integration with Delft-FEWS (used by more than 40 operational forecast centers around the world (Werner et al., 2013)) is demonstrated by the presented case studies. We will also highlight the possibility to give real-time insight into the working of the DA methods applied for supporting the forecaster as mentioned as one of the burning issues by Liu et al., (2012).

The Impact of Land-Atmosphere Coupling on the 2017 Northern Great Plains Drought

NASA Astrophysics Data System (ADS)

Roundy, J. K.; Santanello, J. A., Jr.

2017-12-01

In a changing climate, the potential for increased frequency and duration of drought implies devastating impacts on many aspects of society. The negative impacts of drought can be reduced through informing sustainable water management made possible by real-time monitoring and prediction. The refinement of forecast models is best realized through large-scale observation based datasets, yet there are few of these datasets currently available. The Coupling Drought Index (CDI) is a metric based on the persistence of Land-Atmosphere (L-A) coupling into distinct regimes derived from observations of the land and atmospheric state. The coupling regime persistence has been shown to relate to drought intensification and recovery and is the basis for the Coupling Statistical Model (CSM), which uses a Markov Chain framework to make statistical predictions. The CDI and CSM have been used to understand the predictability of L-A interactions in NCEP's Climate Forecasts System version 2 (CFSv2) and indicated that the forecasts exhibit strong biases in the L-A coupling that produced biases in the precipitation and limited the predictability of drought. The CDI can also be derived exclusively from satellite data which provides an observational large-scale metric of L-A coupling and drought evolution. This provides a unique observational tool for understanding the persistence and intensification of drought through land-atmosphere interactions. During the Spring and Summer of 2017, a drought developed over the Norther great plains that caused substantial agricultural losses in parts of Montana and North and South Dakota. In this work, we use satellite derived CDI to explore the impact of Land-Atmosphere Interactions on the persistence and intensification of the 2017 Northern Great Plains drought. To do this we analyze and quantify the change in CDI at various spatial and temporal scales and correlate these changes with other drought indicators including the U.S. Drought Monitor (http://droughtmonitor.unl.edu). The 2017 Northern Great Plains drought is compared to previous droughts in the region and the predictability of 2017 drought from the CSM as well as future droughts for the area is assessed.

An Assessment of the Skill of GEOS-5 Seasonal Forecasts

NASA Technical Reports Server (NTRS)

Ham, Yoo-Geun; Schubert, Siegfried D.; Rienecker, Michele M.

2013-01-01

The seasonal forecast skill of the NASA Global Modeling and Assimilation Office coupled global climate model (CGCM) is evaluated based on an ensemble of 9-month lead forecasts for the period 1993 to 2010. The results from the current version (V2) of the CGCM consisting of the GEOS-5 AGM coupled to the MOM4 ocean model are compared with those from an earlier version (V1) in which the AGCM (the NSIPP model) was coupled to the Poseidon Ocean Model. It was found that the correlation skill of the Sea Surface Temperature (SST) forecasts is generally better in V2, especially over the sub-tropical and tropical central and eastern Pacific, Atlantic, and Indian Ocean. Furthermore, the improvement in skill in V2 mainly comes from better forecasts of the developing phase of ENSO from boreal spring to summer. The skill of ENSO forecasts initiated during the boreal winter season, however, shows no improvement in terms of correlation skill, and is in fact slightly worse in terms of root mean square error (RMSE). The degradation of skill is found to be due to an excessive ENSO amplitude. For V1, the ENSO amplitude is too strong in forecasts starting in boreal spring and summer, which causes large RMSE in the forecast. For V2, the ENSO amplitude is slightly stronger than that in observations and V1 for forecasts starting in boreal winter season. An analysis of the terms in the SST tendency equation, shows that this is mainly due to an excessive zonal advective feedback. In addition, V2 forecasts that are initiated during boreal winter season, exhibit a slower phase transition of El Nino, which is consistent with larger amplitude of ENSO after the ENSO peak season. It is found that this is due to weak discharge of equatorial Warm Water Volume (WWV). In both observations and V1, the discharge of equatorial WWV leads the equatorial geostrophic easterly current so as to damp the El Nino starting in January. This process is delayed by about 2 months in V2 due to the slower phase transition of the equatorial zonal current from westerly to easterly.

Decadal climate predictions improved by ocean ensemble dispersion filtering

NASA Astrophysics Data System (ADS)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.