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

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.

Assessments of Assimilation of TEMPEST-D into the NCEP Global Forecast System

<p>Unlike large, expensive, and high-risk operational satellites, small/cube satellites (SmallSats) are a small, inexpensive, and a low-risk type of satellite. As a NOAA Cooperative Institute with specialties in satellite data processing and data assimilation, CIRA is funded by a Technology Maturation Program (TMP) research project to help NOAA exploit upcoming constellation of SmallSats to be considered for use in operations. In this research, a CSU-led technology demonstration mission entitled “the Temporal Experiment for Storms and Tropical System - Demonstration (TEMPTEST-D)” is used as an example to explore quick and agile methodologies to entrain SmallSats into the NOAA processing stream. Specifically, a workflow that enables TEMPEST-D data for assimilation into the NCEP Global Forecast System (GFS) with Finite-Volume Cube-Sphered (FV3) dycore (FV3GFS) under the Gridpoint Statistical Interpolation (GSI) based hybrid 4DEnVar system is established.</p><p>One objective of this TMP research project is to assess the impact of SmallSat data on NOAA modeling and assimilation systems used in operations. We begin by asking whether the use of TEMPEST-D data is as good as the use of those obtained from well-established operational satellite sensors. Since the radiometric specification of TEMPEST-D is similar to the Microwave Humidity Sounder (MHS), we address the above question by directly comparing the following three cycled FV3GFS data assimilation and forecasting experiments: 1) the control experiment, which includes all routinely assimilated observations, but only assimilates MHS from the NOAA-19 and MetOp-B platforms, 2) the AddMHS experiment, which is the control plus MHS from the MetOp-A platform, and 3) the AddTEMPEST experiment, which is the control plus TEMPEST-D.</p><p>By differentiating the AddMHS and AddTEMPEST experiments against the control experiment, we will be able to demonstrate that a cost-effective TEMPEST-D is as beneficial as a well-established operational satellite like MHS, in terms of aiding operational global weather forecasting. In addition, results from this research offers implications of the utility of a constellation of SmallSats microwave radiometers for global weather forecasting.  </p>

A Preliminary Assessment of the Impact of Assimilating Satellite Soil Moisture Data Products on 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.

Improving the Stable Surface Layer in the 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.

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

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.

GSI-Based Four-Dimensional Ensemble–Variational (4DEnsVar) Data Assimilation: Formulation and Single-Resolution Experiments with Real Data for NCEP Global Forecast System

A four-dimensional (4D) ensemble–variational data assimilation (DA) system (4DEnsVar) was developed, building upon the infrastructure of the gridpoint statistical interpolation (GSI)-based hybrid DA system. 4DEnsVar used ensemble perturbations valid at multiple time periods throughout the DA window to estimate 4D error covariances during the variational minimization, avoiding the tangent linear and adjoint of the forecast model. The formulation of its implementation in GSI was described. The performance of the system was investigated by evaluating the global forecasts and hurricane track forecasts produced by the NCEP Global Forecast System (GFS) during the 5-week summer period assimilating operational conventional and satellite data. The newly developed system was used to address a few questions regarding 4DEnsVar. 4DEnsVar in general improved upon its 3D counterpart, 3DEnsVar. At short lead times, the improvement over the Northern Hemisphere extratropics was similar to that over the Southern Hemisphere extratropics. At longer lead times, 4DEnsVar showed more improvement in the Southern Hemisphere than in the Northern Hemisphere. The 4DEnsVar showed less impact over the tropics. The track forecasts of 16 tropical cyclones initialized by 4DEnsVar were more accurate than 3DEnsVar after 1-day forecast lead times. The analysis generated by 4DEnsVar was more balanced than 3DEnsVar. Case studies showed that increments from 4DEnsVar using more frequent ensemble perturbations approximated the increments from direct, nonlinear model propagation better than using less frequent ensemble perturbations. Consistently, the performance of 4DEnsVar including both the forecast accuracy and the balances of analyses was in general degraded when less frequent ensemble perturbations were used. The tangent linear normal mode constraint had positive impact for global forecast but negative impact for TC track forecasts.

GSI 3DVar-Based Ensemble–Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments

An ensemble Kalman filter–variational hybrid data assimilation system based on the gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) system was developed. The performance of the system was investigated using the National Centers for Environmental Prediction (NCEP) Global Forecast System model. Experiments covered a 6-week Northern Hemisphere winter period. Both the control and ensemble forecasts were run at the same, reduced resolution. Operational conventional and satellite observations along with an 80-member ensemble were used. Various configurations of the system including one- or two-way couplings, with zero or nonzero weights on the static covariance, were intercompared and compared with the GSI 3DVar system. It was found that the hybrid system produced more skillful forecasts than the GSI 3DVar system. The inclusion of a static component in the background-error covariance and recentering the analysis ensemble around the variational analysis did not improve the forecast skill beyond the one-way coupled system with zero weights on the static covariance. The one-way coupled system with zero static covariances produced more skillful wind forecasts averaged over the globe than the EnKF at the 1–5-day lead times and more skillful temperature forecasts than the EnKF at the 5-day lead time. Sensitivity tests indicated that the difference may be due to the use of the tangent linear normal mode constraint in the variational system. For the first outer loop, the hybrid system showed a slightly slower (faster) convergence rate at early (later) iterations than the GSI 3DVar system. For the second outer loop, the hybrid system showed a faster convergence.