Impact of the Different Components of 4DVAR on the Global Forecast System of the Meteorological Service of Canada

2007 ◽  
Vol 135 (6) ◽  
pp. 2355-2364 ◽  
Author(s):  
Stéphane Laroche ◽  
Pierre Gauthier ◽  
Monique Tanguay ◽  
Simon Pellerin ◽  
Josée Morneau
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Forecast ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Global Forecast System ◽  
Forecast System ◽  
Medium Range Weather Forecast ◽  
Medium Range ◽  
The Impact

Abstract A four-dimensional variational data assimilation (4DVAR) scheme has recently been implemented in the medium-range weather forecast system of the Meteorological Service of Canada (MSC). The new scheme is now composed of several additional and improved features as compared with the three-dimensional variational data assimilation (3DVAR): the first guess at the appropriate time from the full-resolution model trajectory is used to calculate the misfit to the observations; the tangent linear of the forecast model and its adjoint are employed to propagate the analysis increment and the gradient of the cost function over the 6-h assimilation window; a comprehensive set of simplified physical parameterizations is used during the final minimization process; and the number of frequently reported data, in particular satellite data, has substantially increased. The impact of these 4DVAR components on the forecast skill is reported in this article. This is achieved by comparing data assimilation configurations that range in complexity from the former 3DVAR with the implemented 4DVAR over a 1-month period. It is shown that the implementation of the tangent-linear model and its adjoint as well as the increased number of observations are the two features of the new 4DVAR that contribute the most to the forecast improvement. All the other components provide marginal though positive impact. 4DVAR does not improve the medium-range forecast of tropical storms in general and tends to amplify the existing, too early extratropical transition often observed in the MSC global forecast system with 3DVAR. It is shown that this recurrent problem is, however, more sensitive to the forecast model than the data assimilation scheme employed in this system. Finally, the impact of using a shorter cutoff time for the reception of observations, as the one used in the operational context for the 0000 and 1200 UTC forecasts, is more detrimental with 4DVAR. This result indicates that 4DVAR is more sensitive to observations at the end of the assimilation window than 3DVAR.

A New Approach for Estimating the Observation Impact in Ensemble–Variational Data Assimilation

2018 ◽  
Vol 146 (2) ◽  
pp. 447-465 ◽  
Author(s):  
Mark Buehner ◽  
Ping Du ◽  
Joël Bédard
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Observation Error ◽  
New Approach ◽  
Regional Prediction ◽  
Observation Impact ◽  
Adjoint Approach ◽  
The Impact

Abstract Two types of approaches are commonly used for estimating the impact of arbitrary subsets of observations on short-range forecast error. The first was developed for variational data assimilation systems and requires the adjoint of the forecast model. Comparable approaches were developed for use with the ensemble Kalman filter and rely on ensembles of forecasts. In this study, a new approach for computing observation impact is proposed for ensemble–variational data assimilation (EnVar). Like standard adjoint approaches, the adjoint of the data assimilation procedure is implemented through the iterative minimization of a modified cost function. However, like ensemble approaches, the adjoint of the forecast step is obtained by using an ensemble of forecasts. Numerical experiments were performed to compare the new approach with the standard adjoint approach in the context of operational deterministic NWP. Generally similar results are obtained with both approaches, especially when the new approach uses covariance localization that is horizontally advected between analysis and forecast times. However, large differences in estimated impacts are obtained for some surface observations. Vertical propagation of the observation impact is noticeably restricted with the new approach because of vertical covariance localization. The new approach is used to evaluate changes in observation impact as a result of the use of interchannel observation error correlations for radiance observations. The estimated observation impact in similarly configured global and regional prediction systems is also compared. Overall, the new approach should provide useful estimates of observation impact for data assimilation systems based on EnVar when an adjoint model is not available.

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 Analisis Kondisi Atmosfer Terkait Siklon Tropis Pabuk Serta Pengaruhnya Terhadap Tinggi Gelombang di Perairan Kepulauan Riau

2020 ◽  
Vol 8 (2) ◽  
pp. 111
Author(s):  
Diana Cahaya Siregar ◽  
Vivi Putrima Ardah ◽  
Arlin Martha Navitri
Keyword(s):  
Tropical Cyclone ◽  
Satellite Imagery ◽  
Vertical Velocity ◽  
Atmospheric Condition ◽  
Weather Forecast ◽  
Distribution Data ◽  
Medium Range Weather Forecast ◽  
Wavewatch Iii ◽  
Medium Range ◽  
The Impact

Abstract Tropical cyclones is a synoptic scale low pressure system which can have an impact, both directly or indirectly to its traversed area. On January 1 to 6, 2019, Pabuk tropical cyclone was active on the South China Sea which its movement was to the west with its maximum wind speed was 64 knots. The aim of this study was to know the impact of Pabuk tropical cyclone to the atmospheric condition and sea wave on the Riau Islands region. This study used convective index analysis using IR1 channel of Himawari-8 satellite imagery and rainfall distribution data from rainfall observation by meteorological stations which are in the Riau Islands region. European Center for Medium-Range Weather Forecast (ECMWF) reanalysis data likes relative humidity, vertical velocity, and divergence was used to describe the atmospheric condition during the life time of Pabuk tropical cyclone. Wavewatch-III data was used to describe the condition of sea waves on the Riau Islands region. The results showed that Pabuk tropical cyclone had an impact on the growth of convective clouds which it caused the light to moderate rainfall quite evenly in the Riau Islands region. Besides, it was impact to the potential of high waves reached 4.5 meters on the northern of Anambas Sea and 7.0 meters on the north-eastern of Natuna Sea.Key words: Tropical cyclone, satellite imagery, wave height Abstrak Siklon tropis merupakan sistem tekanan rendah berskala sinoptik yang berdampak secara langsung maupun tidak langsung terhadap wilayah yang dilalui. Pada tanggal 1-6 Januari 2019, siklon tropis Pabuk muncul di wilayah Laut Cina Selatan dengan pergerakan ke arah barat dan kecepatan angin maksimumnya mencapai 64 knots. Penelitian ini dilakukan untuk mengkaji dampak yang ditimbulkan oleh siklon tropis Pabuk terhadap kondisi atmosfer dan gelombang laut di wilayah Kepulauan Riau. Penelitian ini menggunakan analisis indeks konvektif dari data citra satelit Himawari-8 kanal IR1 dan analisis sebaran hujan menggunakan data pengamatan curah hujan dari beberapa stasiun meteorologi yang ada di Kepulauan Riau. Data reanalisis European Centre for Medium-Range Weather Forecast (ECMWF) berupa kelembaban udara, vertical velocity, dan divergensi diolah untuk menggambarkan kondisi atmosfer pada masa hidup siklon tropis Pabuk. Data gelombang Wavewatch-III digunakan untuk menggambarkan kondisi gelombang laut di sekitar wilayah Kepulauan Riau. Hasil penelitian menunjukkan bahwa aktifnya siklon tropis Pabuk berdampak terhadap pertumbuhan awan konvektif yang menimbulkan hujan ringan hingga sedang yang cukup merata di wilayah Kepulauan Riau. Selain itu, berdampak juga pada potensi terjadinya gelombang tinggi mencapai 4,5 meter di sebelah utara Perairan Anambas dan 7,0 meter di sebelah timur laut Perairan Natuna.Kata Kunci: Siklon tropis, citra satelit, tinggi gelombang

scholarly journals 4D tropospheric tomography using GPS slant wet delays

2000 ◽  
Vol 18 (2) ◽  
pp. 223-234 ◽  
Author(s):  
A. Flores ◽  
G. Ruffini ◽  
A. Rius
Keyword(s):  
Global Positioning System ◽  
Weather Forecast ◽  
Dense Network ◽  
Medium Range Weather Forecast ◽  
Radio Science ◽  
Global Positioning ◽  
Medium Range ◽  
Instruments And Techniques ◽  
Voxel Grid ◽  
The Impact

Abstract. Tomographic techniques are successfully applied to obtain 4D images of the tropospheric refractivity in a local dense network of global positioning system (GPS) receivers. We show here how GPS data are processed to obtain the tropospheric slant wet delays and discuss the validity of the processing. These slant wet delays are the observables in the tomographic processing. We then discuss the inverse problem in 4D tropospheric tomography making extensive use of simulations to test the system and define the resolution and the impact of noise. Finally, we use data from the Kilauea network in Hawaii for February 1, 1997, and a local 4×4×40 voxel grid on a region of 400 km2 and 15 km in height to produce the corresponding 4D wet refractivity fields, which are then validated using forecast analysis from the European Center for Medium Range Weather Forecast (ECMWF). We conclude that tomographic techniques can be used to monitor the troposphere in time and space.Key words: Radio science (remote sensing; instruments and techniques)

scholarly journals Inclusion of Linearized Moist Physics in NASA’s Goddard Earth Observing System Data Assimilation Tools

2014 ◽  
Vol 142 (1) ◽  
pp. 414-433 ◽  
Author(s):  
Daniel Holdaway ◽  
Ronald Errico ◽  
Ronald Gelaro ◽  
Jong G. Kim
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Weather Forecast ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Adjoint Sensitivity ◽  
Earth Observing System ◽  
Sensitivity Studies ◽  
System Data ◽  
Simple Conversion

Abstract Inclusion of moist physics in the linearized version of a weather forecast model is beneficial in terms of variational data assimilation. Further, it improves the capability of important tools, such as adjoint-based observation impacts and sensitivity studies. A linearized version of the relaxed Arakawa–Schubert (RAS) convection scheme has been developed and tested in NASA’s Goddard Earth Observing System data assimilation tools. A previous study of the RAS scheme showed it to exhibit reasonable linearity and stability. This motivates the development of a linearization of a near-exact version of the RAS scheme. Linearized large-scale condensation is included through simple conversion of supersaturation into precipitation. The linearization of moist physics is validated against the full nonlinear model for 6- and 24-h intervals, relevant to variational data assimilation and observation impacts, respectively. For a small number of profiles, sudden large growth in the perturbation trajectory is encountered. Efficient filtering of these profiles is achieved by diagnosis of steep gradients in a reduced version of the operator of the tangent linear model. With filtering turned on, the inclusion of linearized moist physics increases the correlation between the nonlinear perturbation trajectory and the linear approximation of the perturbation trajectory. A month-long observation impact experiment is performed and the effect of including moist physics on the impacts is discussed. Impacts from moist-sensitive instruments and channels are increased. The effect of including moist physics is examined for adjoint sensitivity studies. A case study examining an intensifying Northern Hemisphere Atlantic storm is presented. The results show a significant sensitivity with respect to moisture.

scholarly journals PREDICTION OF CUMULONIMBUS (CB) CLOUD BASED ON INTEGRATED FORECAST SYSTEM (IFS) OF EUROPEAN MEDIUM-RANGE WEATHER FORECAST (ECMWF) IN THE FLIGHT INFORMATION REGION (FIR) OF JAKARTA AND UJUNG PANDANG

2020 ◽  
Vol 21 (2) ◽  
Author(s):  
Achmad Fahruddin Rais ◽  
Fani Setiawan ◽  
Rezky Yunita ◽  
Erika Meinovelia ◽  
Soenardi Soenardi ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Cloud Cover ◽  
Convective Cloud ◽  
Weather Forecast ◽  
Forecast System ◽  
Medium Range Weather Forecast ◽  
Medium Range ◽  
Ujung Pandang ◽  
Categorical Verification ◽  
Ecmwf Model

This study was focused on cumulonimbus (Cb) cloud prediction based on Integrated Forecast System (IFS) European Medium-Range Weather Forecast (ECMWF) model in the Flight Information Region (FIRs) Jakarta and Ujung Pandang. The Cb cloud prediction was calculated using convective cloud cover (CC) of the precipitation product. The model predictability was examined through categorical verification. The Cb cloud observation was based on brightness temperature (BT) IR1 and brightness temperature difference (BTD) IR1-IR2. The results showed that CC 50%' predictor was the best predictor to estimate the Cb cloud. The study in the period other than 2019 is suggested for the next research because Indian Ocean Dipole (IOD) is extreme that may affect the Cb cloud growth in the study area.

scholarly journals Regional Ensemble–Variational Data Assimilation Using Global Ensemble Forecasts

2017 ◽  
Vol 32 (1) ◽  
pp. 83-96 ◽  
Author(s):  
Wan-Shu Wu ◽  
David F. Parrish ◽  
Eric Rogers ◽  
Ying Lin
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Positive Impact ◽  
Relative Weight ◽  
Variational Data Assimilation ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Data Assimilation System ◽  
Assimilation System

Abstract At the National Centers for Environmental Prediction, the global ensemble forecasts from the ensemble Kalman filter scheme in the Global Forecast System are applied in a regional three-dimensional (3D) and a four dimensional (4D) ensemble–variational (EnVar) data assimilation system. The application is a one-way variational method using hybrid static and ensemble error covariances. To enhance impact, three new features have been added to the existing EnVar system in the Gridpoint Statistical Interpolation (GSI). First, the constant coefficients that assign relative weight between the ensemble and static background error are now allowed to vary in the vertical. Second, a new formulation is introduced for the ensemble contribution to the analysis surface pressure. Finally, in order to make use of the information in the ensemble mean that is disregarded in the existing EnVar in GSI, the trajectory correction, a novel approach, is introduced. Relative to the application of a 3D variational data assimilation algorithm, a clear positive impact on 1–3-day forecasts is realized when applying 3DEnVar analyses in the North American Mesoscale Forecast System (NAM). The 3DEnVar DA system was operationally implemented in the NAM Data Assimilation System in August 2014. Application of a 4DEnVar algorithm is shown to further improve forecast accuracy relative to the 3DEnVar. The approach described in this paper effectively combines contributions from both the regional and the global forecast systems to produce the initial conditions for the regional NAM system.

scholarly journals Impact of Lightning Data Assimilation on the Short-Term Precipitation Forecast over the Central Mediterranean Sea

2021 ◽  
Vol 13 (4) ◽  
pp. 682
Author(s):  
Rosa Claudia Torcasio ◽  
Stefano Federico ◽  
Albert Comellas Prat ◽  
Giulia Panegrossi ◽  
Leo Pio D'Adderio ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Mediterranean Sea ◽  
Positive Impact ◽  
Weather Forecast ◽  
Precipitation Forecast ◽  
Time Interval ◽  
Precipitation Pattern ◽  
Central Mediterranean ◽  
Central Mediterranean Sea ◽  
The Impact

Lightning data assimilation (LDA) is a powerful tool to improve the weather forecast of convective events and has been widely applied with this purpose in the past two decades. Most of these applications refer to events hitting coastal and land areas, where people live. However, a weather forecast over the sea has many important practical applications, and this paper focuses on the impact of LDA on the precipitation forecast over the central Mediterranean Sea around Italy. The 3 h rapid update cycle (RUC) configuration of the weather research and forecasting (WRF) model) has been used to simulate the whole month of November 2019. Two sets of forecasts have been considered: CTRL, without lightning data assimilation, and LIGHT, which assimilates data from the LIghtning detection NETwork (LINET). The 3 h precipitation forecast has been compared with observations of the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) (IMERG) dataset and with rain gauge observations recorded in six small Italian islands. The comparison of CTRL and LIGHT precipitation forecasts with the IMERG dataset shows a positive impact of LDA. The correlation between predicted and observed precipitation improves over wide areas of the Ionian and Adriatic Seas when LDA is applied. Specifically, the correlation coefficient for the whole domain increases from 0.59 to 0.67, and the anomaly correlation (AC) improves by 5% over land and by 8% over the sea when lightning is assimilated. The impact of LDA on the 3 h precipitation forecast over six small islands is also positive. LDA improves the forecast by both decreasing the false alarms and increasing the hits of the precipitation forecast, although with variability among the islands. The case study of 12 November 2019 (time interval 00–03 UTC) has been used to show how important the impact of LDA can be in practice. In particular, the shifting of the main precipitation pattern from land to the sea caused by LDA gives a much better representation of the precipitation field observed by the IMERG precipitation product.

scholarly journals Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments

2010 ◽  
Vol 138 (5) ◽  
pp. 1550-1566 ◽  
Author(s):  
Mark Buehner ◽  
P. L. Houtekamer ◽  
Cecilien Charette ◽  
Herschel L. Mitchell ◽  
Bin He
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Spatial Covariance ◽  
4D Flow ◽  
Background Error ◽  
Single Observation ◽  
The Impact

Abstract An intercomparison of the Environment Canada variational and ensemble Kalman filter (EnKF) data assimilation systems is presented in the context of global deterministic NWP. In an EnKF experiment having the same spatial resolution as the inner loop in the four-dimensional variational data assimilation system (4D-Var), the mean of each analysis ensemble is used to initialize the higher-resolution deterministic forecasts. Five different variational data assimilation experiments are also conducted. These include both 4D-Var and 3D-Var (with first guess at appropriate time) experiments using either (i) prescribed background-error covariances similar to those used operationally, which are static in time and include horizontally homogeneous and isotropic correlations; or (ii) flow-dependent covariances computed from the EnKF background ensembles with spatial covariance localization applied. The fifth variational data assimilation experiment is a new approach called the Ensemble-4D-Var (En-4D-Var). This approach uses 4D flow-dependent background-error covariances estimated from EnKF ensembles to produce a 4D analysis without the need for tangent-linear or adjoint versions of the forecast model. In this first part of a two-part paper, results from a series of idealized assimilation experiments are presented. In these experiments, only a single observation or vertical profile of observations is assimilated to explore the impact of various fundamental differences among the EnKF and the various variational data assimilation approaches considered. In particular, differences in the application of covariance localization in the EnKF and variational approaches are shown to have a significant impact on the assimilation of satellite radiance observations. The results also demonstrate that 4D-Var and the EnKF can both produce similar 4D background-error covariances within a 6-h assimilation window. In the second part, results from medium-range deterministic forecasts for the study period of February 2007 are presented for the EnKF and the five variational data assimilation approaches considered.

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