scholarly journals Atlantic Tropical Cyclogenetic Processes during SOP-3 NAMMA in the GEOS-5 Global Data Assimilation and Forecast System

2009 ◽  
Vol 66 (12) ◽  
pp. 3563-3578 ◽  
Author(s):  
Oreste Reale ◽  
William K. Lau ◽  
Kyu-Myong Kim ◽  
Eugenia Brin
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Thermal Structure ◽  
Tropical Cyclogenesis ◽  
Critical Threshold ◽  
Forecast System ◽  
Earth Observing System ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The One ◽  
Global Data

Abstract This article investigates the role of the Saharan air layer (SAL) in tropical cyclogenetic processes associated with a nondeveloping and a developing African easterly wave observed during the Special Observation Period (SOP-3) phase of the 2006 NASA African Monsoon Multidisciplinary Analyses (NAMMA). The two waves are chosen because they both interact heavily with Saharan air. A global data assimilation and forecast system, the NASA Goddard Earth Observing System, version 5 (GEOS-5), is being run to produce a set of high-quality global analyses, inclusive of all observations used operationally but with additional satellite information. In particular, following previous works by the same authors, the quality-controlled data from the Atmospheric Infrared Sounder (AIRS) used to produce these analyses have a better coverage than the one adopted by operational centers. From these improved analyses, two sets of 31 five-day high-resolution forecasts, at horizontal resolutions of both half and quarter degrees, are produced. Results indicate that very steep moisture gradients are associated with the SAL in forecasts and analyses, even at great distances from their source over the Sahara. In addition, a thermal dipole in the vertical (warm above, cool below) is present in the nondeveloping case. The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra and Aqua satellites shows that aerosol optical thickness, indicative of more dust as opposed to other factors, is higher in the nondeveloping case. Altogether, results suggest that the radiative effect of dust may play some role in producing a thermal structure less favorable to cyclogenesis. Results also indicate that only global horizontal resolutions on the order of 20–30 km can capture the large-scale transport and the fine thermal structure of the SAL, inclusive of the sharp moisture gradients, reproducing the effect of tropical cyclone suppression that has been hypothesized by previous authors from observational and regional modeling perspectives. These effects cannot be fully represented at lower resolutions, therefore global resolution of a quarter of a degree is a minimum critical threshold necessary to investigate Atlantic tropical cyclogenesis from a global modeling perspective.

scholarly journals Ensemble Kalman Filtering with One-Step-Ahead Smoothing

2018 ◽  
Vol 146 (2) ◽  
pp. 561-581 ◽  
Author(s):  
Naila F. Raboudi ◽  
Boujemaa Ait-El-Fquih ◽  
Ibrahim Hoteit
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Sequential Data ◽  
Bayesian Filtering ◽  
Error Statistics ◽  
Background Error ◽  
One Step ◽  
The One ◽  
Lorenz 96 ◽  
Ensemble Kalman Filtering

The ensemble Kalman filter (EnKF) is widely used for sequential data assimilation. It operates as a succession of forecast and analysis steps. In realistic large-scale applications, EnKFs are implemented with small ensembles and poorly known model error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance the data assimilation performance of EnKFs. Filtering with OSA smoothing introduces an updated step with future observations, conditioning the ensemble sampling with more information. This should provide an improved background ensemble in the analysis step, which may help to mitigate the suboptimal character of EnKF-based methods. Here, the authors demonstrate the efficiency of a stochastic EnKF with OSA smoothing for state estimation. They then introduce a deterministic-like EnKF-OSA based on the singular evolutive interpolated ensemble Kalman (SEIK) filter. The authors show that the proposed SEIK-OSA outperforms both SEIK, as it efficiently exploits the data twice, and the stochastic EnKF-OSA, as it avoids observational error undersampling. They present extensive assimilation results from numerical experiments conducted with the Lorenz-96 model to demonstrate SEIK-OSA’s capabilities.

scholarly journals A Linearized Prognostic Cloud Scheme in NASA’s Goddard Earth Observing System Data Assimilation Tools

2015 ◽  
Vol 143 (10) ◽  
pp. 4198-4219 ◽  
Author(s):  
Daniel Holdaway ◽  
Ronald Errico ◽  
Ronald Gelaro ◽  
Jong G. Kim ◽  
Rahul Mahajan
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Cloud Fraction ◽  
Lead Times ◽  
Convection Scheme ◽  
Earth Observing System ◽  
Advection Diffusion ◽  
System Data ◽  
Replacement Scheme ◽  
Perturbation Growth

Abstract A linearized prognostic cloud scheme has been developed to accompany the linearized convection scheme recently implemented in NASA’s Goddard Earth Observing System data assimilation tools. The linearization, developed from the nonlinear cloud scheme, treats cloud variables prognostically so they are subject to linearized advection, diffusion, generation, and evaporation. Four linearized cloud variables are modeled, the ice and water phases of clouds generated by large-scale condensation and, separately, by detraining convection. For each species the scheme models their sources, sublimation, evaporation, and autoconversion. Large-scale, anvil and convective species of precipitation are modeled and evaporated. The cloud scheme exhibits linearity and realistic perturbation growth, except around the generation of clouds through large-scale condensation. Discontinuities and steep gradients are widely used here and severe problems occur in the calculation of cloud fraction. For data assimilation applications this poor behavior is controlled by replacing this part of the scheme with a perturbation model. For observation impacts, where efficiency is less of a concern, a filtering is developed that examines the Jacobian. The replacement scheme is only invoked if Jacobian elements or eigenvalues violate a series of tuned constants. The linearized prognostic cloud scheme is tested by comparing the linear and nonlinear perturbation trajectories for 6-, 12-, and 24-h forecast times. The tangent linear model performs well and perturbations of clouds are well captured for the lead times of interest.

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

2013 ◽  
Vol 141 (11) ◽  
pp. 4098-4117 ◽  
Author(s):  
Xuguang Wang ◽  
David Parrish ◽  
Daryl Kleist ◽  
Jeffrey Whitaker
Keyword(s):  
Data Assimilation ◽  
Hybrid System ◽  
Coupled System ◽  
Global Forecast System ◽  
Forecast System ◽  
Outer Loop ◽  
Hybrid Data Assimilation ◽  
Hybrid Data ◽  
The One ◽  
Ncep Global Forecast System

Abstract 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.

scholarly journals Impact of Adaptively Thinned AIRS Cloud-Cleared Radiances on Tropical Cyclone Representation in a Global Data Assimilation and Forecast System

2018 ◽  
Vol 33 (4) ◽  
pp. 909-931 ◽  
Author(s):  
Oreste Reale ◽  
Erica L. McGrath-Spangler ◽  
Will McCarty ◽  
Daniel Holdaway ◽  
Ronald Gelaro
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Forecast Model ◽  
Adaptive Strategy ◽  
Atmospheric Infrared Sounder ◽  
Full Agreement ◽  
Clear Sky ◽  
Earth Observing System ◽  
Moving Domain ◽  
Global Data

Abstract A simple adaptive thinning methodology for Atmospheric Infrared Sounder (AIRS) radiances is evaluated through a combination of observing system experiments (OSEs) and adjoint methodologies. The OSEs are performed with the NASA Goddard Earth Observing System (GEOS, version 5) data assimilation and forecast model. In addition, the adjoint-based forecast sensitivity observation impact technique is applied to assess fractional contributions of sensors in different thinning configurations. The adaptive strategy uses a denser AIRS coverage in a moving domain centered around tropical cyclones (TCs) but sparser everywhere else. The OSEs consist of two sets of data assimilation runs that cover the period from 1 September to 10 November 2014, with the first 20 days discarded for spinup. Both sets assimilate all conventional and satellite observations used operationally. In addition, one ingests clear-sky AIRS radiances, the other cloud-cleared radiances, each comprising multiple thinning strategies. Daily 7-day forecasts are initialized from all these analyses and evaluated with a focus on TCs over the Atlantic and Pacific. Evidence is provided on the effectiveness of this simple TC-centered adaptive radiance thinning strategy, in full agreement with previous theoretical studies. Specifically, global skill increases, and tropical cyclone representation is substantially improved. The improvement is particularly strong when cloud-cleared radiances are assimilated. Finally, the article suggests that cloud-cleared radiances, if thinned more aggressively than the currently used clear-sky radiances, could successfully replace them with large improvements in TC forecasting and no loss of global skill.

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