Sentences with phrase «for seasonal predictions»

Our results strongly suggest that it does, which has implications for seasonal predictions.»

Many of the models are becoming useful for seasonal predictions, namely ENSO.

The forecast scheme for the September sea ice extent is based on a methodology similar to one used for the seasonal prediction of river streamflow.

Ionita and Grosfeld (IUP Bremen Data), 4.25 (3.53 - 4.97), Statistical The forecast scheme for the September sea ice extent is based on a methodology similar to one used for the seasonal prediction of river streamflow (Ionita et al., 2008, 2014).

Not exact matches

Jason - 3 measurements will also be ingested by Numerical prediction models coupling the atmosphere and the oceans used for seasonal forecasting.

«Hotspots show that vegetation alters climate by up to 30 percent: Engineers find strong feedbacks between the atmosphere and vegetation that explain up to 30 % of precipitation and surface radiation variance; study reveals large potential for improving seasonal weather predictions.»

If there's a big volcanic eruption tomorrow, Robock said he could make predictions for seasonal temperatures, precipitation and the appearance of El Niño next winter.

NOAA's NWS Climate Prediction Center (CPC) is responsible for issuing seasonal climate outlook maps for one to thirteen months in the future.

Palmer, T.N., et al., 2004: Development of a European multimodel ensemble system for seasonal to interannual prediction (DEMETER).

A few climate models have been tested for (and shown) capability in initial value predictions, on time scales from weather forecasting (a few days) to seasonal forecasting (annual).

On Thursday, the Climate Prediction Center (CPC) released its latest seasonal drought forecast for the U.S..

PIOMAS has been run in a forward mode (and hence without data assimilation) to yield seasonal predictions for the sea ice outlook (Zhang et al. 2008) and has also provided input to statistical forecasts (Lindsay et al. 2008) and fully - coupled models.

For the future, data assimilation might help us to keep the state of a climate model closer to the real world's, allowing us to improve predictions on seasonal and decadal time scales.

In making its seasonal prediction, forecasters cautioned that there are large portions of the country for which there are no clear indications whether it will be a warmer, colder, wetter, or drier than average winter, largely due to a fickle El Niño event that may have petered out too early to have much of an impact on North American winter weather.

The subseasonal to seasonal timescale provides a unique opportunity to capitalise on the expertise of the weather and climate research communities, and to bring them together to improve predictions on a timescale of particular relevance to the Global Framework for Climate Services (GFCS).

«Accurate seasonal and decadal predictions of tropical cyclone activity are essential for the development of mitigation strategies for the 2.7 billion residents living within cyclone prone regions.

A top - down climate effect that shows long - term drift (and may also be out of phase with the bottom - up solar forcing) would change the spatial response patterns and would mean that climate - chemistry models that have sufficient resolution in the stratosphere would become very important for making accurate regional / seasonal climate predictions.

Speakers included Center for Climate and Life Fellow Andrew Robertson, a senior research scientist at the International Research Institute for Climate and Society (IRI), who presented his research in a session on seasonal fire prediction.

The meeting will mainly cover the following themes, but can include other topics related to understanding and modelling the atmosphere: ● Surface drag and momentum transport: orographic drag, convective momentum transport ● Processes relevant for polar prediction: stable boundary layers, mixed - phase clouds ● Shallow and deep convection: stochasticity, scale - awareness, organization, grey zone issues ● Clouds and circulation feedbacks: boundary - layer clouds, CFMIP, cirrus ● Microphysics and aerosol - cloud interactions: microphysical observations, parameterization, process studies on aerosol - cloud interactions ● Radiation: circulation coupling; interaction between radiation and clouds ● Land - atmosphere interactions: Role of land processes (snow, soil moisture, soil temperature, and vegetation) in sub-seasonal to seasonal (S2S) prediction ● Physics - dynamics coupling: numerical methods, scale - separation and grey - zone, thermodynamic consistency ● Next generation model development: the challenge of exascale, dynamical core developments, regional refinement, super-parametrization ● High Impact and Extreme Weather: role of convective scale models; ensembles; relevant challenges for model development

Lee, A.E. MacDonald, P. Madden, J. Middlecoff, J. Rosinski, T.G. Smirnova, S. Sun, and N. Wang, 2015: A vertically flow - following, icosahedral - grid model for medium - range and seasonal prediction.

Mikhail Tolstykh is an expert for global numerical weather prediction models to develop medium - range and seasonal forecasts.

Type 3 dynamic downscaling takes lateral boundary conditions from a global model prediction forced by specified real world surface boundary conditions, such as for seasonal weather predictions based on observed sea surface temperatures, but the initial observed atmospheric conditions in the global model are forgotten.

Within the confines of our work with RASM and CESM, we will: (i) quantify the added value of using regional models for downscaling arctic simulations from global models, (ii) address the impacts of high resolution, improved process representations and coupling between model components on predictions at seasonal to decadal time scales, (iii) identify the most important processes essential for inclusion in future high resolution GC / ESMs, e.g. ACME, using CESM as a test bed, and (iv) better quantify the relationship between skill and uncertainty in the Arctic Region for high fidelity models.

This strategy for developing confidence is being extended to seasonal climate prediction models, which are based on coupled atmosphere / ocean models.

The results of these flights and ancillary data were released in May for inclusion in seasonal ice prediction models.

Sugiura N., T. Awaji, S. Masuda, T. Mochizuki, T. Toyoda, T. Miyama, H. Igarashi, Y. Ishikawa, 2008: Development of a four - dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations.

There are a number of partners involved in this project including the International Research Institute for Climate and Society (IRI), IGAD (Intergovernmental Authority on Development) Climate Prediction Applications Centre (ICPAC), the meteorological services of Ethiopia, Kenya, Tanzania and Uganda, educational institutions and end users of seasonal forecasting information in the region and from the four countries.

CanCM4 is the latest component of the Canadian Seasonal to Inter-annual Prediction System (CanSIPS), which is a multi-seasonal climate prediction system developed particularly for Canada but applicablePrediction System (CanSIPS), which is a multi-seasonal climate prediction system developed particularly for Canada but applicableprediction system developed particularly for Canada but applicable globally.

CAS = Commission for Atmospheric Sciences CMDP = Climate Metrics and Diagnostic Panel CMIP = Coupled Model Intercomparison Project DAOS = Working Group on Data Assimilation and Observing Systems GASS = Global Atmospheric System Studies panel GEWEX = Global Energy and Water Cycle Experiment GLASS = Global Land - Atmosphere System Studies panel GOV = Global Ocean Data Assimilation Experiment (GODAE) Ocean View JWGFVR = Joint Working Group on Forecast Verification Research MJO - TF = Madden - Julian Oscillation Task Force PDEF = Working Group on Predictability, Dynamics and Ensemble Forecasting PPP = Polar Prediction Project QPF = Quantitative precipitation forecast S2S = Subseasonal to Seasonal Prediction Project SPARC = Stratospheric Processes and their Role in Climate TC = Tropical cyclone WCRP = World Climate Research Programme WCRP Grand Science Challenges • Climate Extremes • Clouds, Circulation and Climate Sensitivity • Melting Ice and Global Consequences • Regional Sea - Ice Change and Coastal Impacts • Water Availability WCRP JSC = Joint Scientific Committee WGCM = Working Group on Coupled Modelling WGSIP = Working Group on Subseasonal to Interdecadal Prediction WWRP = World Weather Research Programme YOPP = Year of Polar Prediction

The Polar Prediction Project (PPP) is a 10 - year (2013 — 2022) endeavour of the World Meteorological Organization's (WMO) World Weather Research Programme (WWRP) with the aim of promoting cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours toPrediction Project (PPP) is a 10 - year (2013 — 2022) endeavour of the World Meteorological Organization's (WMO) World Weather Research Programme (WWRP) with the aim of promoting cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours toprediction services for the polar regions, on time scales from hours to seasonal.

Overall, the CM - HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skilful hydrologic predictions.

Overall, the CM - HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions

Do you at least agree that the better analogy for climate prediction is seasonal change than weather patterns?

The high correlation between the Snow Advance Index and the winter AO demonstrates that the AO is mostly predictable, which can be exploited for skillful seasonal climate predictions.

As a result of soil and atmosphere feedbacks (Beljaars et al. 1996; Seneviratne et al. 2010), seasonal predictions of soil moisture content over the US can further increase the predictability of precipitation and atmospheric temperature variations for up to several months (Zeng et al. 1999; Kanamitsu et al. 2003; Koster and Suarez 2003; Yang et al. 2004; Dirmeyer et al. 2013).

RE: 4th Error -RCB- Poses an objection to the non-scientific term catastrophic [NOTE: Scientific «consensus» is often being used & / or implied in standard climate - change discourse - Yet Consensus is a Political Term - NOT a Scientific Term]- HOWEVER - When Jim Hansen, the IPCC & Al Gore, et - al - go from predicting 450 — 500 ppm CO2 to 800 — 1000ppm by the end of the 21st century -LCB- said to the be highest atmospheric CO2 content in 20 — 30 Million YRS -RCB-; — & estimates for aver global temps by 21st century's end go from 2 * C to 6 * C to 10 * C; — & increased sea level estimates go from 10 - 20 cm to 50 - 60 cm to 1M — 2M -LCB- which would totally submerge the Maldives & partially so Bangladesh -RCB-; — predictions of the total melting of the Himalayan Ice caps by 2050, near total melting of Greenland's ice sheet & partial melting of Antarctica's ice sheet before the 21st century's end; — massive crop failures; — more intense & frequent hurricane -LCB- ala Katrina -RCB- for much longer seasonal durations, etc, etc, etc... — IMO That's Sounds pretty damned CATASTROPHIC to ME!

Those include: USDA National Nutrient Database for Standard Reference Germplasm Resources Information Network Farm Program Atlas Climate Prediction Center (CPC) U.S. Seasonal Drought Outlook (SDO) 2012 Census of Agriculture World Agricultural Production... Continued

Among those in attendance was Phil Klotzbach, a hurricane researcher at Colorado State University who now does much of the research for Bill Gray's seasonal hurricane predictions, the oldest and best - known annual forecast.

Models include the Geophysical Fluid Dynamics Laboratory (GFDL) model, the National Aeronautics and Space Administration (NASA) Seasonal to Interannual Prediction Program (NSIPP) model, the National Center for Atmospheric Research Community Atmosphere Model (CAM3), the Canadian Centre for Climate Modelling and Analysis (CCCma) model, the Centre for Climate System Research (CCSR) model, the Bureau of Meteorology Research Centre (BMRC) model and the Hadley Centre Atmospheric Model version 3 (HadAM3).

In the longer term — statistical correlation of SST and rainfall are probably the way to go for seasonal to decadal predictions.

The purpose of the seasonal predictions of arctic sea ice is for scientific research and education only.

But Joseph D'Aleo, co-founder of the Weather Channel and chief forecaster at Weatherbell Analytics, a meteorological consulting firm, called NOAA's seasonal forecast for December through February «nonsense» - pointing out that NOAA's predictions have been proven wrong the past two winters.

WMO coordinates efforts for meeting the needs for climate information, such as for climate monitoring, climate - data management, climate - change detection, seasonal - to - interannual climate predictions, and assessments of the impacts of climate change.

He co-chaired the the international Working Group on Seasonal to Interannual Prediction for several years and has been awarded academic prizes such as the Royal Met Society's Adrian Gill Prize.

This has proven very effective for seasonal to interannual prediction, especially given the need for very large sets of simulations to assess the skill of a prediction system.

GFDL scientists focus on model - building relevant for society, such as hurricane research, weather and ocean prediction, seasonal forecasting, and understanding global and regional climate change.

The prediction is for a roughly sinusoidal seasonal temperature cycle, based on solar zenith angle.

The ECMWF atmospheric module is evaluated every day in numerical weather prediction mode, and also each month for the seasonal forecasts.