scholarly journals About the possibility of using the output data of the global atmosphere model GFS NCEP in ecological research

2021 ◽  
Author(s):  
М.Ч. Залиханов ◽  
А.Х. Кагермазов ◽  
Л.Т. Созаева ◽  
К.М. Беккиев
Keyword(s):  
Correlation Analysis ◽  
Atmospheric Pollution ◽  
Meteorological Parameters ◽  
Dew Point ◽  
Actual Data ◽  
Global Forecast System ◽  
Forecast System ◽  
Atmosphere Model ◽  
Environmental Prediction ◽  
Ncep Global Forecast System

Проведена оценка степени совпадения прогностических значений стратификации атмосферы с нарастающей заблаговременностью 24 часа, полученных из глобальной модели атмосферы GFS NCEP (Global Forecast System National Centers for Environmental Prediction) с фактическими данными аэрологического зондирования на основе корреляционного анализа. Актуальность работы заключается в том, что в настоящее время количество опасных природных явлений продолжает увеличиваться, в том числе и загрязнение атмосферы примесями, приводящими к глобальному потеплению. При прогнозировании опасных явлений для экологии входными данными являются значения полей метеопараметров по фактическим данным аэрологического зондирования атмосферы. Такие данные доступны только на отдельных метеостанциях, расположенных достаточно далеко друг от друга, что усложняет проведение исследований. Между тем инструменты для анализа и оценки распространения и рассеивания загрязняющих веществ в атмосфере в настоящее время получили значительное развитие. Сдерживающим фактором их более широкого применения заинтересованными структурами по прогнозированию качества воздуха, аварийно-спасательными службами, представителями авиации, государственными учреждениями и сообществом исследователей атмосферы является недостаток информации о текущем состоянии атмосферы, а также получение прогностических метеопараметров. Для решения этой проблемы предлагаются использовать данные глобальной модели атмосферы GFS NCEP. Целью исследования является определить правомерность замены фактических данных аэрологического зондирования атмосферы на прогностические поля стратифицированных метеопараметров из глобальной модели атмосферы. Методом исследования является один из методов статистического анализа данных - корреляционный анализ. В результате исследований получено, что коэффициенты корреляции между прогностическими и фактическими значениями температуры воздуха, температуры точки росы, скорости и направления ветра имеют высокие значения. Это делает возможными использование данных глобальной модели при математическом моделировании распространения загрязнения в атмосфере, а также прогнозе опасных стихийных явлений, таких как паводок, сильный ливень, град, сель, приводящих к нарушению природных экологических систем. The degree of matching of the predictive values of atmosphere stratification with an increasing lead time of 24 hours obtained from the global atmosphere model GFS NCEP (Global Forecast System National Centers for Environmental Prediction) and the actual data of aerological sounding based on correlation analysis was assessed. The relevance of the work lies in the fact that at present the number of natural hazards continues to increase, including atmospheric pollution with impurities leading to global warming. When predicting dangerous phenomena for the environment, the input data are the values of the fields of meteorological parameters based on the actual data of the aerological sounding of the atmosphere. Such data is available only at individual weather stations located far enough apart from each other, which complicates the research. Meanwhile, tools for analyzing and assessing the spread and dispersion of pollutants in the atmosphere have now received significant development. A limiting factor in their wider use by interested structures for predicting air quality, emergency services, aviation representatives, government agencies and the community of atmosphere researchers is the lack of information about the current state of the atmosphere, as well as obtaining predictive meteorological parameters. To solve this problem, data from the global atmosphere model GFS NCEP are proposed. The aim of the study is to determine the validity of replacing the actual data of the aerological sounding of the atmosphere with the predictive fields of stratified meteorological parameters from the global atmosphere model. The research method is correlation analysis, one of the methods of statistical data analysis. As a result of the research, it was found that the correlation coefficients between the predictive and actual values of air temperature, dew point temperature, wind speed and direction have high values. This makes it possible to use the data of the global model in mathematical modeling of atmospheric pollution, as well as the forecast of dangerous natural phenomena, such as floods, heavy rain, hail, mudslides, leading to disruption of natural ecological systems.

scholarly journals A Multiconserving Discretization with Enthalpy as a Thermodynamic Prognostic Variable in Generalized Hybrid Vertical Coordinates for the NCEP Global Forecast System

2011 ◽  
Vol 139 (5) ◽  
pp. 1583-1607 ◽  
Author(s):  
Hann-Ming Henry Juang
Keyword(s):  
Implicit Time ◽  
Global Forecast System ◽  
Forecast System ◽  
Vertical Coordinate ◽  
Prognostic Variable ◽  
Specific Hybrid ◽  
Environmental Prediction ◽  
Daily Forecast ◽  
Isentropic Coordinates ◽  
Ncep Global Forecast System

A new vertical discretization used in the atmospheric dynamics of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) is illustrated, with enthalpy as the thermodynamic prognostic variable to reduce computation in thermodynamic equations while concerning all gas tracers in the model. Mass, energy, entropy, and angular momentum conservations are utilized as constraints to discretize the vertical integration with a finite-difference scheme. A specific definition of a generalized hybrid vertical coordinate, including sigma, isobaric, and isentropic surfaces, is introduced to define pressure at the model levels. Vertical fluxes are obtained by the equation of local changes in variables defined for vertical coordinates at all model layers. The forward-weighting semi-implicit time scheme is utilized to eliminate computational noise for stable integration. Because of time splitting between the dynamic and physics processes, the vertical advection is required both in the model dynamics and model physics, and the semi-implicit time scheme is used both in dynamics and after physics computation. Three configurations—sigma, sigma pressure, and sigma entropy—from the specific hybrid vertical coordinates with layer definition similar to NCEP operational GFS have been implemented in the NCEP GFS. Results from the sigma-isentropic coordinate show the largest anomaly correlation and the smallest root-mean-square error in tropical wind among all results at all layers, especially the upper layers. The scores from a period of daily forecast up to 5 days with the sigma-isentropic coordinate show the same level of skill as compared to the NCEP operational GFS. The results from the hurricane tracks for the fall of 2005 with sigma-isentropic coordinates show better scores compared with the operational GFS.

scholarly journals Ensemble Data Assimilation with the NCEP Global Forecast System

2008 ◽  
Vol 136 (2) ◽  
pp. 463-482 ◽  
Author(s):  
Jeffrey S. Whitaker ◽  
Thomas M. Hamill ◽  
Xue Wei ◽  
Yucheng Song ◽  
Zoltan Toth
Keyword(s):  
Data Assimilation ◽  
Southern Hemisphere ◽  
Global Forecast System ◽  
Forecast System ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Triangular Truncation ◽  
Data Assimilation System ◽  
Ncep Global Forecast System ◽  
Assimilation System

Abstract Real-data experiments with an ensemble data assimilation system using the NCEP Global Forecast System model were performed and compared with the NCEP Global Data Assimilation System (GDAS). All observations in the operational data stream were assimilated for the period 1 January–10 February 2004, except satellite radiances. Because of computational resource limitations, the comparison was done at lower resolution (triangular truncation at wavenumber 62 with 28 levels) than the GDAS real-time NCEP operational runs (triangular truncation at wavenumber 254 with 64 levels). The ensemble data assimilation system outperformed the reduced-resolution version of the NCEP three-dimensional variational data assimilation system (3DVAR), with the biggest improvement in data-sparse regions. Ensemble data assimilation analyses yielded a 24-h improvement in forecast skill in the Southern Hemisphere extratropics relative to the NCEP 3DVAR system (the 48-h forecast from the ensemble data assimilation system was as accurate as the 24-h forecast from the 3DVAR system). Improvements in the data-rich Northern Hemisphere, while still statistically significant, were more modest. It remains to be seen whether the improvements seen in the Southern Hemisphere will be retained when satellite radiances are assimilated. Three different parameterizations of background errors unaccounted for in the data assimilation system (including model error) were tested. Adding scaled random differences between adjacent 6-hourly analyses from the NCEP–NCAR reanalysis to each ensemble member (additive inflation) performed slightly better than the other two methods (multiplicative inflation and relaxation-to-prior).

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

2016 ◽  
Vol 113 (42) ◽  
pp. 11765-11769 ◽  
Author(s):  
Banglin Zhang ◽  
Richard S. Lindzen ◽  
Vijay Tallapragada ◽  
Fuzhong Weng ◽  
Qingfu Liu ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Forecast Model ◽  
Vertical Resolution ◽  
Global Forecast System ◽  
Forecast System ◽  
Atlantic Basin ◽  
Hurricane Track ◽  
Environmental Prediction ◽  
The Impact ◽  
Positive Impacts

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.

scholarly journals Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of convective storms

2021 ◽  
Author(s):  
Ivette H. Banos ◽  
Will D. Mayfield ◽  
Guoqing Ge ◽  
Luiz F. Sapucci ◽  
Jacob R. Carley ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Current Data ◽  
Dew Point ◽  
Squall Line ◽  
Forecast System ◽  
Background Error ◽  
Convective Storms ◽  
Error Covariance ◽  
Convective Scale ◽  
Environmental Prediction

Abstract. The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional and convective scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to the current operational suite, each component of the RRFS needs to be configured through exhaustive testing and evaluation. The current data assimilation component uses the Gridpoint Statistical Interpolation (GSI) system. In this study, various data assimilation algorithms and configurations in GSI are assessed for their impacts on RRFS analyses and forecasts of a squall line over Oklahoma on 4 May 2020. Results show that a baseline RRFS run without data assimilation is able to represent the observed convection, but with stronger cells and large location errors. With data assimilation, these errors are reduced, especially in the 4 and 6 h forecasts using 75 % of the ensemble background error covariance (BEC) and with the supersaturation removal function activated in GSI. Decreasing the vertical ensemble localization radius in the first 10 layers of the hybrid analysis results in overall less skillful forecasts. Convection and precipitation are overforecast in most forecast hours when using planetary boundary layer pseudo-observations, but the root mean square error and bias of the 2 h forecast of 2 m dew point temperature are reduced by 1.6 K during the afternoon hours. Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble BEC in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC. Our results provide insight into current capabilities of the RRFS data assimilation system and identify configurations that should be considered as candidates for the first version of RRFS.

Assimilation of TRMM Multisatellite Precipitation Analysis with a Low-Resolution NCEP Global Forecast System

2016 ◽  
Vol 144 (2) ◽  
pp. 643-661 ◽  
Author(s):  
Guo-Yuan Lien ◽  
Takemasa Miyoshi ◽  
Eugenia Kalnay
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Cumulative Distribution ◽  
Precipitation Data ◽  
Global Forecast System ◽  
Low Resolution ◽  
Forecast System ◽  
Original Solution ◽  
Precipitation Analysis ◽  
Ncep Global Forecast System

Abstract Current methods of assimilation of precipitation into numerical weather prediction models are able to make the model precipitation become similar to the observed precipitation during the assimilation, but the model forecasts tend to return to their original solution after a few hours. To facilitate the precipitation assimilation, a logarithm transformation has been used in several past studies. Lien et al. proposed instead to assimilate precipitation using the local ensemble transform Kalman filter (LETKF) with a Gaussian transformation technique and succeeded in improving the model forecasts in perfect-model observing system simulation experiments (OSSEs). In this study, the method of Lien et al. is tested within a more realistic configuration: the TRMM Multisatellite Precipitation Analysis (TMPA) data are assimilated into a low-resolution version of the NCEP Global Forecast System (GFS). With guidance from a statistical study comparing the GFS model background precipitation and the TMPA data, some modifications of the assimilation methods proposed in Lien et al. are made, including 1) applying separate Gaussian transformations to model and to observational precipitation based on their own cumulative distribution functions; 2) adopting a quality control criterion based on the correlation between the long-term model and observed precipitation data at the observation location; and 3) proposing a new method to define the transformation of zero precipitation that takes into account the zero precipitation probability in the background ensemble rather than the climatology. With these modifications, the assimilation of the TMPA precipitation data improves both the analysis and 5-day model forecasts when compared with a control experiment assimilating only rawinsonde data.

Predictability of ECMWF Cubic Octahedral Nature Run (ECO1280) using Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) Compared to Real-World Predictability

2020 ◽  
Author(s):  
Sean Casey ◽  
Lidia Cucurull ◽  
Andres Vidal
Keyword(s):  
Finite Volume ◽  
Real World ◽  
Forecast Model ◽  
Global Forecast System ◽  
Forecast System ◽  
Weather Forecasts ◽  
Global Nature ◽  
System Assessment ◽  
Cubed Sphere ◽  
Environmental Prediction

<p>Under the Quantitative Observing System Assessment Program, the National Oceanic and Atmospheric Administration's (NOAA's) Atlantic Oceanographic and Meteorological Laboratory (AOML) is preparing to utilize the 9-km-resolution European Centre for Medium-Range Weather Forecasts (ECWMF) Cubic Octahedral grid global Nature Run (ECO1280) for observation simulation and conducting Observing System Simulation Experiments (OSSEs).   As part of the OSSE calibration, and before experiments can be run, it needs to be shown that the forecast model used in the OSSEs does not do a better job in predicting the Nature Run meteorology than it does in predicting the real world. Otherwise, the conclusions from the OSSEs in such a configuration may misstate the potential impact of a given instrument. In this presentation, the predictability of the new global OSSE system being developed at NOAA will be discussed. The NOAA/National Centers for Environmental Prediction (NCEP) Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) is used to test predictability over the first two months of ECO1280 (October-November 2015), comparing forecasts using simulated observations with added errors to real-world observations.  Only conventional observations will be utilized in both cases.  </p>

scholarly journals A Preliminary Assessment of the Impact of Assimilating Satellite Soil Moisture Data Products on NCEP Global Forecast System

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Weizhong Zheng ◽  
Xiwu Zhan ◽  
Jicheng Liu ◽  
Michael Ek
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Prediction Models ◽  
Precipitation Forecast ◽  
Global Forecast System ◽  
Forecast System ◽  
Numerical Weather ◽  
Soil Moisture Data ◽  
Data Products ◽  
Ncep Global Forecast System

It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models. Several microwave satellite soil moisture retrieval data products have also been available for applications. However, these observational data products have not been employed in any operational numerical weather or climate prediction models. In this study, a preliminary test of assimilating satellite soil moisture data products from the NOAA-NESDIS Soil Moisture Operational Product System (SMOPS) into the NOAA-NCEP Global Forecast System (GFS) is conducted. Using the ensemble Kalman filter (EnKF) introduced in recent year publications and implemented in the GFS, the multiple satellite blended daily global soil moisture data from SMOPS for the month of April 2012 are assimilated into the GFS. The forecasts of surface variables, anomaly correlations of isobar heights, and precipitation forecast skills of the GFS with and without the soil moisture data assimilation are assessed. The surface and deep layer soil moisture estimates of the GFS after the satellite soil moisture assimilation are found to have slightly better agreement with the ground soil moisture measurements at dozens of sites across the continental United States (CONUS). Forecasts of surface humidity and air temperature, 500 hPa height anomaly correlations, and the precipitation forecast skill demonstrated certain level of improvements after the soil moisture assimilation against those without the soil moisture assimilation. However, the methodology for the soil moisture data assimilation into operational GFS runs still requires further development efforts and tests.

scholarly journals Improving the Stable Surface Layer in the NCEP Global Forecast System

2017 ◽  
Vol 145 (10) ◽  
pp. 3969-3987 ◽  
Author(s):  
Weizhong Zheng ◽  
Michael Ek ◽  
Kenneth Mitchell ◽  
Helin Wei ◽  
Jesse Meng
Keyword(s):  
Surface Layer ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Global Forecast System ◽  
Surface Cooling ◽  
Forecast System ◽  
Near Surface ◽  
Stable Surface ◽  
Stable Surface Layer ◽  
Ncep Global Forecast System

This study examines the performance of the NCEP Global Forecast System (GFS) surface layer parameterization scheme for strongly stable conditions over land in which turbulence is weak or even disappears because of high near-surface atmospheric stability. Cases of both deep snowpack and snow-free conditions are investigated. The results show that decoupling and excessive near-surface cooling may appear in the late afternoon and nighttime, manifesting as a severe cold bias of the 2-m surface air temperature that persists for several hours or more. Concurrently, because of negligible downward heat transport from the atmosphere to the land, a warm temperature bias develops at the first model level. The authors test changes to the stable surface layer scheme that include introduction of a stability parameter constraint that prevents the land–atmosphere system from fully decoupling and modification to the roughness-length formulation. GFS sensitivity runs with these two changes demonstrate the ability of the proposed surface layer changes to reduce the excessive near-surface cooling in forecasts of 2-m surface air temperature. The proposed changes prevent both the collapse of turbulence in the stable surface layer over land and the possibility of numerical instability resulting from thermal decoupling between the atmosphere and the surface. The authors also execute and evaluate daily GFS 7-day test forecasts with the proposed changes spanning a one-month period in winter. The assessment reveals that the systematic deficiencies and substantial errors in GFS near-surface 2-m air temperature forecasts are considerably reduced, along with a notable reduction of temperature errors throughout the lower atmosphere and improvement of forecast skill scores for light and medium precipitation amounts.

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