Impact of a Cold Pool Parameterization on the Diurnal Cycle and Intraseasonal Variability in the GEOS AGCM

A gradual transition from shallow to deep convection may be important both to the continental diurnal cycle of precipitation and to the tropical Madden-Julian Oscillation. However, many existing convection parameterizations transition too readily, with corresponding diurnal and intraseasonal biases. High entrainment rates can be used to delay deep convection, but typically produce mean state biases; this is the "entrainment dilemma." Cold pools and sub-grid organization offer a potential solution to this dilemma, and recent work shows parameterized cold pools can effectively modulate deep convection, with improvements to the diurnal cycle and intraseasonal variability. Here we investigate the effects of a simple prognostic cold pool scheme coupled to the Grell-Freitas convection parameterization, in a set of global simulations with the NASA GEOS model. Air detrained from parameterized downdrafts is maintained in vertically resolved cold pools, which evolve with simplified dynamics. We test several options for cold pool feedbacks on convection, including modifications to deep convective entrainment rates, convective source air properties, and thermodynamic profiles, based on the level of cold pool activity. Cold pool impacts on the diurnal cycle are evaluated against TRMM, and moisture and moist static energy budgets are used to understand changes in tropical intraseasonal variability. Preliminary results show delays in the diurnal cycle of precipitation

Parameterization of sub-grid scale convection

The following topics are discussed: an overview of the cumulus parameterization problem; interactions between explicit and implicit processes in mesoscale models; effects of model grid size on the cumulus parameterization problem; parameterizing convective effects on momentum fields in mesoscale models; differences between slantwise and vertical cumulus parameterization; experiments with different closure hypotheses; and coupling cumulus parameterizations to boundary layer, stable cloud, and radiation schemes

Improving dust simulations in WRF-Chem v4.1.3 coupled with the GOCART aerosol module

In this paper, we rectify inconsistencies that emerge in the Weather Research and Forecasting model with chemistry (WRF-Chem)&nbsp;v3.2 code when using the Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol module. These inconsistencies have been reported, and corrections have been implemented in WRF-Chem&nbsp;v4.1.3. Here, we use a WRF-Chem experimental setup configured over the Middle East (ME) to estimate the effects of these inconsistencies. Firstly, we show that the old version underestimates the PM2.5 diagnostic output by 7&thinsp;% and overestimates PM10 by 5&thinsp;% in comparison with the corrected one. Secondly, we demonstrate that submicron dust particles' contribution was incorrectly accounted for in the calculation of optical properties. Therefore, aerosol optical depth (AOD) in the old version was 25&thinsp;%&ndash;30&thinsp;% less than in the corrected one. Thirdly, we show that the gravitational settling procedure, in comparison with the corrected version, caused higher dust column loadings by 4&thinsp;%&ndash;6&thinsp;%, PM10 surface concentrations by 2&thinsp;%&ndash;4&thinsp;%, and mass of the gravitationally settled dust by 5&thinsp;%&ndash;10&thinsp;%. The cumulative effect of the found inconsistencies led to the significantly higher dust content in the atmosphere in comparison with the corrected WRF-Chem version. Our results explain why in many WRF-Chem simulations PM10 concentrations were exaggerated. We present the methodology for calculating diagnostics we used to estimate the impacts of introduced code modifications. We share the developed Merra2BC interpolator, which allows processing Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) output for constructing initial and boundary conditions for chemical species and aerosols. &nbsp;</p

The Grell-Freitas Convection Parameterization: Recent Developments and Applications Within the NASA GEOS Global Model

We implemented and began to evaluate an alternative convection parameterization for the NASA Goddard Earth Observing System (GEOS) global model. The parameterization is based on the mass flux approach with several closures, for equilibrium and non-equilibrium convection, and includes scale and aerosol awareness functionalities. Recently, the scheme has been extended to a tri-modal spectral size approach to simulate the transition from shallow, mid, and deep convection regimes. In addition, the inclusion of a new closure for non-equilibrium convection resulted in a substantial gain of realism in model simulation of the diurnal cycle of convection over the land. Here, we briefly introduce the recent developments, implementation, and preliminary results of this parameterization in the NASA GEOS modeling system

Techniques and resources for storm-scale numerical weather prediction

The topics discussed include the following: multiscale application of the 5th-generation PSU/NCAR mesoscale model, the coupling of nonhydrostatic atmospheric and hydrostatic ocean models for air-sea interaction studies; a numerical simulation of cloud formation over complex topography; adaptive grid simulations of convection; an unstructured grid, nonhydrostatic meso/cloud scale model; efficient mesoscale modeling for multiple scales using variable resolution; initialization of cloud-scale models with Doppler radar data; and making effective use of future computing architectures, networks, and visualization software

Subseasonal Forecasting with an Icosahedral, Vertically Quasi-Lagrangian Coupled Model. Part I: Model Overview and Evaluation of Systematic Errors

The atmospheric hydrostatic Flow-Following Icosahedral Model (FIM), developed for medium-range weather prediction, provides a unique three-dimensional grid structurea quasi-uniform icosahedral horizontal grid and an adaptive quasi-Lagrangian vertical coordinate. To extend the FIM framework to subseasonal time scales, an icosahedral-grid rendition of the Hybrid Coordinate Ocean Model (iHYCOM) was developed and coupled to FIM. By sharing a common horizontal mesh, airsea fluxes between the two models are conserved locally and globally. Both models use similar adaptive hybrid vertical coordinates. Another unique aspect of the coupled model (referred to as FIMiHYCOM) is the use of the GrellFreitas scale-aware convective scheme in the atmosphere. A multiyear retrospective study is necessary to demonstrate the potential usefulness and allow for immediate bias correction of a subseasonal prediction model. In these two articles, results are shown based on a 16-yr period of hindcasts from FIMiHYCOM, which has been providing real-time forecasts out to a lead time of 4 weeks for NOAAs Subseasonal Experiment (SubX) starting July 2017. Part I provides an overview of FIMiHYCOM and compares its systematic errors at subseasonal time scales to those of NOAAs operational Climate Forecast System version 2 (CFSv2). Part II uses bias-corrected hindcasts to assess both deterministic and probabilistic subseasonal skill of FIMiHYCOM. FIMiHYCOM has smaller biases than CFSv2 for some fields (including precipitation) and comparable biases for other fields (including sea surface temperature). FIMiHYCOM also has less drift in bias between weeks 1 and 4 than CFSv2. The unique grid structure and physics suite of FIMiHYCOM is expected to add diversity to multimodel ensemble forecasts at subseasonal time scales in SubX

Weights Estimation by Firefly with Predation Optimization for Ensemble Precipitation Prediction Using Brams

Uma média ponderada usando diferentes esquemas de convecção pode ser usada em previsão. O problema inverso de estimação de parâmetros é formulado pela a diferença quadrática entre a precipitação medida e a calculada. A função objetivo é minimizada pelo Algoritmo Firefly com Predação. O método é aplicado ao código BRAMS. The precipitation prediction is addressed by weighted average. The weight identification Is a parameter estimation inverse problem formulated by the square difference between measurements and computed precipitations. The metaheuristic Firefly Algorithm with Predation (FAP) is used to compute the best weights. The method is applied to the BRAMS code

Constraining Black Carbon Aerosol over Asia using OMI Aerosol Absorption Optical Depth and the Adjoint of GEOS-Chem

Accurate estimates of the emissions and distribution of black carbon (BC) in the region referred to here as Southeastern Asia (70degE-l50degE, 11degS-55degN) are critical to studies of the atmospheric environment and climate change. Analysis of modeled BC concentrations compared to in situ observations indicates levels are underestimated over most of Southeast Asia when using any of four different emission inventories. We thus attempt to reduce uncertainties in BC emissions and improve BC model simulations by developing top-down, spatially resolved, estimates of BC emissions through assimilation of OMI observations of aerosol absorption optical depth (AAOD) with the GEOS-Chem model and its adjoint for April and October of 2006. Overwhelming enhancements, up to 500%, in anthropogenic BC emissions are shown after optimization over broad areas of Southeast Asia in April. In October, the optimization of anthropogenic emissions yields a slight reduction (1-5%) over India and parts of southern China, while emissions increase by 10-50% over eastern China. Observational data from in situ measurements and AERONET observations are used to evaluate the BC inversions and assess the bias between OMI and AERONET AAOD. Low biases in BC concentrations are improved or corrected in most eastern and central sites over China after optimization, while the constrained model still underestimates concentrations in Indian sites in both April and October, possibly as a. consequence of low prior emissions. Model resolution errors may contribute up to a factor of 2.5 to the underestimate of surface BC concentrations over northern India. We also compare the optimized results using different anthropogenic emission inventories and discuss the sensitivity of top-down constraints on anthropogenic emissions with respect to biomass burning emissions. In addition, the impacts of brown carbon, the formulation of the observation operator, and different a priori constraints on the optimization are investigated. Overall, despite these limitations and uncertainties, using OMI AAOD to constrain BC sources improves model representation of BC distributions, particularly over China