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

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.

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

Monthly Weather Review ◽

10.1175/mwr-d-13-00193.1 ◽

2014 ◽

Vol 142 (1) ◽

pp. 414-433 ◽

Cited By ~ 18

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.

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

Journal of the Atmospheric Sciences ◽

10.1175/2009jas3123.1 ◽

2009 ◽

Vol 66 (12) ◽

pp. 3563-3578 ◽

Cited By ~ 28

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.

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PowerSystems.jl — A power system data management package for large scale modeling

SoftwareX ◽

10.1016/j.softx.2021.100747 ◽

2021 ◽

Vol 15 ◽

pp. 100747

Author(s):

José Daniel Lara ◽

Clayton Barrows ◽

Daniel Thom ◽

Dheepak Krishnamurthy ◽

Duncan Callaway

Keyword(s):

Power System ◽

Data Management ◽

Large Scale ◽

Scale Modeling ◽

System Data ◽

Large Scale Modeling

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Observed decrease in atmospheric mercury explained by global decline in anthropogenic emissions

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1516312113 ◽

2016 ◽

Vol 113 (3) ◽

pp. 526-531 ◽

Cited By ~ 175

Author(s):

Yanxu Zhang ◽

Daniel J. Jacob ◽

Hannah M. Horowitz ◽

Long Chen ◽

Helen M. Amos ◽

...

Keyword(s):

North America ◽

Large Scale ◽

Elemental Mercury ◽

Atmospheric Mercury ◽

Small Scale ◽

Commercial Products ◽

Earth Observing System ◽

Global Emission ◽

Emission Controls ◽

Phase Out

Observations of elemental mercury (Hg0) at sites in North America and Europe show large decreases (∼1–2% y−1) from 1990 to present. Observations in background northern hemisphere air, including Mauna Loa Observatory (Hawaii) and CARIBIC (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container) aircraft flights, show weaker decreases (<1% y−1). These decreases are inconsistent with current global emission inventories indicating flat or increasing emissions over that period. However, the inventories have three major flaws: (i) they do not account for the decline in atmospheric release of Hg from commercial products; (ii) they are biased in their estimate of artisanal and small-scale gold mining emissions; and (iii) they do not properly account for the change in Hg0/HgII speciation of emissions from coal-fired utilities after implementation of emission controls targeted at SO2 and NOx. We construct an improved global emission inventory for the period 1990 to 2010 accounting for the above factors and find a 20% decrease in total Hg emissions and a 30% decrease in anthropogenic Hg0 emissions, with much larger decreases in North America and Europe offsetting the effect of increasing emissions in Asia. Implementation of our inventory in a global 3D atmospheric Hg simulation [GEOS-Chem (Goddard Earth Observing System-Chemistry)] coupled to land and ocean reservoirs reproduces the observed large-scale trends in atmospheric Hg0 concentrations and in HgII wet deposition. The large trends observed in North America and Europe reflect the phase-out of Hg from commercial products as well as the cobenefit from SO2 and NOx emission controls on coal-fired utilities.

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Using Stochastically Generated Subcolumns to Represent Cloud Structure in a Large-Scale Model

Monthly Weather Review ◽

10.1175/mwr3257.1 ◽

2006 ◽

Vol 134 (12) ◽

pp. 3644-3656 ◽

Cited By ~ 52

Author(s):

Robert Pincus ◽

Richard Hemler ◽

Stephen A. Klein

Keyword(s):

Large Scale ◽

Statistical Tests ◽

Internal Variability ◽

Cloud Fraction ◽

Shortwave Radiation ◽

Scale Model ◽

Cloud Structure ◽

Model Configuration ◽

The Impact ◽

Radiation Scheme

Abstract A new method for representing subgrid-scale cloud structure in which each model column is decomposed into a set of subcolumns has been introduced into the Geophysical Fluid Dynamics Laboratory’s global atmospheric model AM2. Each subcolumn in the decomposition is homogeneous, but the ensemble reproduces the initial profiles of cloud properties including cloud fraction, internal variability (if any) in cloud condensate, and arbitrary overlap assumptions that describe vertical correlations. These subcolumns are used in radiation and diagnostic calculations and have allowed the introduction of more realistic overlap assumptions. This paper describes the impact of these new methods for representing cloud structure in instantaneous calculations and long-term integrations. Shortwave radiation computed using subcolumns and the random overlap assumption differs in the global annual average by more than 4 W m−2 from the operational radiation scheme in instantaneous calculations; much of this difference is counteracted by a change in the overlap assumption to one in which overlap varies continuously with the separation distance between layers. Internal variability in cloud condensate, diagnosed from the mean condensate amount and cloud fraction, has about the same effect on radiative fluxes as does the ad hoc tuning accounting for this effect in the operational radiation scheme. Long simulations with the new model configuration show little difference from the operational model configuration, while statistical tests indicate that the model does not respond systematically to the sampling noise introduced by the approximate radiative transfer techniques introduced to work with the subcolumns.

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TREATMENT OF THE ERROR DUE TO UNRESOLVED SCALES IN SEQUENTIAL DATA ASSIMILATION

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127411030775 ◽

2011 ◽

Vol 21 (12) ◽

pp. 3619-3626 ◽

Cited By ~ 7

Author(s):

ALBERTO CARRASSI ◽

STÉPHANE VANNITSEM

Keyword(s):

Dynamical System ◽

Kalman Filter ◽

Data Assimilation ◽

Large Scale ◽

Model Error ◽

Environmental Modeling ◽

Sequential Data ◽

Chaotic Dynamical System ◽

Error Covariance ◽

Sequential Data Assimilation

In this paper, a method to account for model error due to unresolved scales in sequential data assimilation, is proposed. An equation for the model error covariance required in the extended Kalman filter update is derived along with an approximation suitable for application with large scale dynamics typical in environmental modeling. This approach is tested in the context of a low order chaotic dynamical system. The results show that the filter skill is significantly improved by implementing the proposed scheme for the treatment of the unresolved scales.

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Interpretation of Adaptive Observing Guidance for Atlantic Tropical Cyclones

Monthly Weather Review ◽

10.1175/2007mwr2027.1 ◽

2007 ◽

Vol 135 (12) ◽

pp. 4006-4029 ◽

Cited By ~ 26

Author(s):

C. A. Reynolds ◽

M. S. Peng ◽

S. J. Majumdar ◽

S. D. Aberson ◽

C. H. Bishop ◽

...

Keyword(s):

Data Assimilation ◽

Total Energy ◽

Tropical Cyclones ◽

Error Variance ◽

Naval Research ◽

Error Statistics ◽

Background Error ◽

Analysis Error ◽

The Impact ◽

Perturbation Growth

Abstract Adaptive observing guidance products for Atlantic tropical cyclones are compared using composite techniques that allow one to quantitatively examine differences in the spatial structures of the guidance maps and relate these differences to the constraints and approximations of the respective techniques. The guidance maps are produced using the ensemble transform Kalman filter (ETKF) based on ensembles from the National Centers for Environmental Prediction and the European Centre for Medium-Range Weather Forecasts (ECMWF), and total-energy singular vectors (TESVs) produced by ECMWF and the Naval Research Laboratory. Systematic structural differences in the guidance products are linked to the fact that TESVs consider the dynamics of perturbation growth only, while the ETKF combines information on perturbation evolution with error statistics from an ensemble-based data assimilation scheme. The impact of constraining the SVs using different estimates of analysis error variance instead of a total-energy norm, in effect bringing the two methods closer together, is also assessed. When the targets are close to the storm, the TESV products are a maximum in an annulus around the storm, whereas the ETKF products are a maximum at the storm location itself. When the targets are remote from the storm, the TESVs almost always indicate targets northwest of the storm, whereas the ETKF targets are more scattered relative to the storm location and often occur over the northern North Atlantic. The ETKF guidance often coincides with locations in which the ensemble-based analysis error variance is large. As the TESV method is not designed to consider spatial differences in the likely analysis errors, it will produce targets over well-observed regions, such as the continental United States. Constraining the SV calculation using analysis error variance values from an operational 3D variational data assimilation system (with stationary, quasi-isotropic background error statistics) results in a modest modulation of the target areas away from the well-observed regions, and a modest reduction of perturbation growth. Constraining the SVs using the ETKF estimate of analysis error variance produces SV targets similar to ETKF targets and results in a significant reduction in perturbation growth, due to the highly localized nature of the analysis error variance estimates. These results illustrate the strong sensitivity of SVs to the norm (and to the analysis error variance estimate used to define it) and confirm that discrepancies between target areas computed using different methods reflect the mathematical and physical differences between the methods themselves.

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A Scale-Selective Data Assimilation Approach to Improving Tropical Cyclone Track and Intensity Forecasts in a Limited-Area Model: A Case Study of Hurricane Felix (2007)

Weather and Forecasting ◽

10.1175/waf-d-10-05033.1 ◽

2012 ◽

Vol 27 (1) ◽

pp. 124-140 ◽

Cited By ~ 11

Author(s):

Bin Liu ◽

Lian Xie

Keyword(s):

Data Assimilation ◽

Large Scale ◽

Dynamical Downscaling ◽

Weather Forecasting ◽

Forecast Error ◽

Regional Model ◽

Small Scale ◽

Limited Area ◽

Track Forecast ◽

Global Models

Abstract Accurately forecasting a tropical cyclone’s (TC) track and intensity remains one of the top priorities in weather forecasting. A dynamical downscaling approach based on the scale-selective data assimilation (SSDA) method is applied to demonstrate its effectiveness in TC track and intensity forecasting. The SSDA approach retains the merits of global models in representing large-scale environmental flows and regional models in describing small-scale characteristics. The regional model is driven from the model domain interior by assimilating large-scale flows from global models, as well as from the model lateral boundaries by the conventional sponge zone relaxation. By using Hurricane Felix (2007) as a demonstration case, it is shown that, by assimilating large-scale flows from the Global Forecast System (GFS) forecasts into the regional model, the SSDA experiments perform better than both the original GFS forecasts and the control experiments, in which the regional model is only driven by lateral boundary conditions. The overall mean track forecast error for the SSDA experiments is reduced by over 40% relative to the control experiments, and by about 30% relative to the GFS forecasts, respectively. In terms of TC intensity, benefiting from higher grid resolution that better represents regional and small-scale processes, both the control and SSDA runs outperform the GFS forecasts. The SSDA runs show approximately 14% less overall mean intensity forecast error than do the control runs. It should be noted that, for the Felix case, the advantage of SSDA becomes more evident for forecasts with a lead time longer than 48 h.

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Cluster analysis of midlatitude oceanic cloud regimes: mean properties and temperature sensitivity

Atmospheric Chemistry and Physics ◽

10.5194/acp-10-6435-2010 ◽

2010 ◽

Vol 10 (13) ◽

pp. 6435-6459 ◽

Cited By ~ 19

Author(s):

N. D. Gordon ◽

J. R. Norris

Keyword(s):

Large Scale ◽

Longwave Radiation ◽

Cloud Fraction ◽

Climate System ◽

Radiative Properties ◽

Upper Troposphere ◽

Low Level ◽

Increasing Temperature ◽

Cloud Regimes ◽

Cloud Response

Abstract. Clouds play an important role in the climate system by reducing the amount of shortwave radiation reaching the surface and the amount of longwave radiation escaping to space. Accurate simulation of clouds in computer models remains elusive, however, pointing to a lack of understanding of the connection between large-scale dynamics and cloud properties. This study uses a k-means clustering algorithm to group 21 years of satellite cloud data over midlatitude oceans into seven clusters, and demonstrates that the cloud clusters are associated with distinct large-scale dynamical conditions. Three clusters correspond to low-level cloud regimes with different cloud fraction and cumuliform or stratiform characteristics, but all occur under large-scale descent and a relatively dry free troposphere. Three clusters correspond to vertically extensive cloud regimes with tops in the middle or upper troposphere, and they differ according to the strength of large-scale ascent and enhancement of tropospheric temperature and humidity. The final cluster is associated with a lower troposphere that is dry and an upper troposphere that is moist and experiencing weak ascent and horizontal moist advection. Since the present balance of reflection of shortwave and absorption of longwave radiation by clouds could change as the atmosphere warms from increasing anthropogenic greenhouse gases, we must also better understand how increasing temperature modifies cloud and radiative properties. We therefore undertake an observational analysis of how midlatitude oceanic clouds change with temperature when dynamical processes are held constant (i.e., partial derivative with respect to temperature). For each of the seven cloud regimes, we examine the difference in cloud and radiative properties between warm and cold subsets. To avoid misinterpreting a cloud response to large-scale dynamical forcing as a cloud response to temperature, we require horizontal and vertical temperature advection in the warm and cold subsets to have near-median values in three layers of the troposphere. Across all of the seven clusters, we find that cloud fraction is smaller and cloud optical thickness is mostly larger for the warm subset. Cloud-top pressure is higher for the three low-level cloud regimes and lower for the cirrus regime. The net upwelling radiation flux at the top of the atmosphere is larger for the warm subset in every cluster except cirrus, and larger when averaged over all clusters. This implies that the direct response of midlatitude oceanic clouds to increasing temperature acts as a negative feedback on the climate system. Note that the cloud response to atmospheric dynamical changes produced by global warming, which we do not consider in this study, may differ, and the total cloud feedback may be positive.

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Cloud and Precipitation Parameterizations in Modeling and Variational Data Assimilation: A Review

Journal of the Atmospheric Sciences ◽

10.1175/2006jas2030.1 ◽

2007 ◽

Vol 64 (11) ◽

pp. 3766-3784 ◽

Cited By ~ 32

Author(s):

Philippe Lopez

Keyword(s):

Data Assimilation ◽

General Circulation ◽

Large Scale ◽

Weather Prediction ◽

General Circulation Models ◽

Variational Data Assimilation ◽

Current Status ◽

General Question ◽

Observation Error ◽

Moist Processes

Abstract This paper first reviews the current status, issues, and limitations of the parameterizations of atmospheric large-scale and convective moist processes that are used in numerical weather prediction and climate general circulation models. Both large-scale (resolved) and convective (subgrid scale) moist processes are dealt with. Then, the general question of the inclusion of diabatic processes in variational data assimilation systems is addressed. The focus is put on linearity and resolution issues, the specification of model and observation error statistics, the formulation of the control vector, and the problems specific to the assimilation of observations directly affected by clouds and precipitation.

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