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At least 307 records · Page 17

A Realization of Bias Correction Method in the GMAO Coupled System

Over the past several decades, a tremendous effort has been made to improve model performance in the simulation of the climate system. The cold or warm sea surface temperature (SST) bias in the tropics is still a problem common to most coupled ocean atmosphere general circulation models (CGCMs). The precipitation biases in CGCMs are also accompanied by SST and surface wind biases. The deficiencies and biases over the equatorial oceans through their influence on the Walker circulation likely contribute the precipitation biases over land surfaces. In this study, we introduce an approach in the CGCM modeling to correct model biases. This approach utilizes the history of the model's short-term forecasting errors and their seasonal dependence to modify model's tendency term and to minimize its climate drift. The study shows that such an approach removes most of model climate biases. A number of other aspects of the model simulation (e.g. extratropical transient activities) are also improved considerably due to the imposed pre-processed initial 3-hour model drift corrections. Because many regional biases in the GEOS-5 CGCM are common amongst other current models, our approaches and findings are applicable to these other models as well.

Chang, Yehui↗

Determining Cloud Thermodynamic Phase from Micropulse Lidar Network Data

Determining cloud thermodynamic phase is a critical factor in studies of Earth's radiation budget. Here we use observations from the NASA Micro Pulse Lidar Network (MPLNET) and thermodynamic profiles from the Goddard Earth Observing System, version 5 (GEOS-5) to distinguish liquid water, mixed-phase, and ice water clouds. The MPLNET provides sparse global, autonomous, and continuous measurements of clouds and aerosols which have been used in a number of scientific investigations to date. The use of a standardized instrument and a common suite of data processing algorithms with thorough uncertainty characterization allows for straightforward comparisons between sites. Lidars with polarization capabilities have recently been incorporated into the MPLNET project which allows, for the first time, the ability to infer a cloud thermodynamic phase. This presentation will look specifically at the occurrence of ice and mixed phase clouds in the temperature region of -10 C to -40 C for different climatological regions and seasons. We compare MPLNET occurrences of mixed-phase clouds to an historical climatology based on observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) spacecraft.

mixing layer height↗

Application of Aura OMI L2G Products Compared with NASA MERRA-2 Assimilation

The Ozone Monitoring Instrument (OMI) is one of the instruments aboard NASA's Aura satellite. It measures ozone total column and vertical profile, aerosols, clouds, and trace gases including NO2, SO2, HCHO, BrO, and OClO using absorption in the ultraviolet electromagnetic spectrum (280 - 400 nm). OMI Level-2G (L2G) products are based on the pixel-level OMI granule satellite measurements stored within global 0.25 deg. X 0.25 deg. grids, therefore they conserve all the Level 2 (L2) spatial and temporal details for 24 hours of scientific data in one file. The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) is NASA's atmospheric reanalysis, using an upgraded version of Goddard Earth Observing System Model, version 5 (GEOS-5) data assimilation system. MERRA-2 includes aerosol data reanalysis and improved representations of stratospheric ozone, compared with its predecessor MERRA, in both instantaneous and time-averaged collections. It is found that simply comparing satellite Level-3 products might cause biases, due to lack of detailed temporal and original retrieval information. It is therefore preferable to inter-compare or implement satellite derived physical quantities directly with/to model assimilation with as high temporal and spatial resolutions as possible. This study will demonstrate utilization of OMI L2G daily aerosol and ozone products by comparing them with MERRA-2 hourly aerosol/ozone simulations, matched in both space and time aspects. Both OMI and MERRA-2 products are accessible online through NASA Goddard Earth Sciences Data Information Services Center (GES DISC, https://disc.gsfc.nasa.gov/).

OMI L2G products↗

How Can TOLNet Help to Better Understand Tropospheric Ozone? A Satellite Perspective

Potential sources of a priori ozone (O3) profiles for use in Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite tropospheric O3 retrievals are evaluated with observations from multiple Tropospheric Ozone Lidar Network (TOLNet) systems in North America. An O3 profile climatology (tropopause-based O3 climatology (TB-Clim), currently proposed for use in the TEMPO O3 retrieval algorithm) derived from ozonesonde observations and O3 profiles from three separate models (operational Goddard Earth Observing System (GEOS-5) Forward Processing (FP) product, reanalysis product from Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), and the GEOS-Chem chemical transport model (CTM)) were: 1) evaluated with TOLNet measurements on various temporal scales (seasonally, daily, hourly) and 2) implemented as a priori information in theoretical TEMPO tropospheric O3 retrievals in order to determine how each a priori impacts the accuracy of retrieved tropospheric (0-10 km) and lowermost tropospheric (LMT, 0-2 km) O3 columns. We found that all sources of a priori O3 profiles evaluated in this study generally reproduced the vertical structure of summer-averaged observations. However, larger differences between the a priori profiles and lidar observations were observed when evaluating inter-daily and diurnal variability of tropospheric O3. The TB-Clim O3 profile climatology was unable to replicate observed inter-daily and diurnal variability of O3 while model products, in particular GEOS-Chem simulations, displayed more skill in reproducing these features. Due to the ability of models, primarily the CTM used in this study, on average to capture the inter-daily and diurnal variability of tropospheric and LMT O3 columns, using a priori profiles from CTM simulations resulted in TEMPO retrievals with the best statistical comparison with lidar observations. Furthermore, important from an air quality perspective, when high LMT O3 values were observed, using CTM a priori profiles resulted in TEMPO LMT O3 retrievals with the least bias. The application of time-specific (non-climatological) hourly/daily model predictions as the a priori profile in TEMPO O3 retrievals will be best suited when applying this data to study air quality or event-based processes as the standard retrieval algorithm will still need to use a climatology product. Follow-on studies to this work are currently being conducted to investigate the application of different CTM-predicted O3 climatology products in the standard TEMPO retrieval algorithm. Finally, similar methods to those used in this study can be easily applied by TEMPO data users to recalculate tropospheric O3 profiles provided from the standard retrieval using a different source of a priori.

Satellite↗

Global Soil Moisture Estimation from L-Band Satellite Data: The Impact of Radiative Transfer Modeling in Assimilation and Retrieval Systems

The SMOS and SMAP missions have collected a wealth of global L-band Brightness temperature (Tb) observations. The retrieval of surface Soil moisture estimates, and the estimation of other geophysical Variables, such as root-zone soil moisture and temperature, via data Assimilation into land surface models largely depends on accurate Radiative transfer modeling (RTM). This presentation will focus on various configuration aspects of the RTM (i) for the inversion of SMOS Tb to surface soil moisture, and (ii) for the forward modeling as part of a SMOS Tb data assimilation System to estimate a consistent set of geophysical land surface Variables, using the GEOS-5 Catchment Land Surface Model.

De Lannoy, Gabrielle↗

Improved Hydrology over Peatlands in a Global Land Modeling System

Peatlands of the Northern Hemisphere represent an important carbon pool that mainly accumulated since the last ice age under permanently wet conditions in specific geological and climatic settings. The carbon balance of peatlands is closely coupled to water table dynamics. Consequently, the future carbon balance over peatlands is strongly dependent on how hydrology in peatlands will react to changing boundary conditions, e.g. due to climate change or regional water level drawdown of connected aquifers or streams. Global land surface modeling over organic-rich regions can provide valuable global-scale insights on where and how peatlands are in transition due to changing boundary conditions. However, the current global land surface models are not able to reproduce typical hydrological dynamics in peatlands well. We implemented specific structural and parametric changes to account for key hydrological characteristics of peatlands into NASA's GEOS-5 Catchment Land Surface Model (CLSM, Koster et al. 2000). The main modifications pertain to the modeling of partial inundation, and the definition of peatland-specific runoff and evapotranspiration schemes. We ran a set of simulations on a high performance cluster using different CLSM configurations and validated the results with a newly compiled global in-situ dataset of water table depths in peatlands. The results demonstrate that an update of soil hydraulic properties for peat soils alone does not improve the performance of CLSM over peatlands. However, structural model changes for peatlands are able to improve the skill metrics for water table depth. The validation results for the water table depth indicate a reduction of the bias from 2.5 to 0.2 m, and an improvement of the temporal correlation coefficient from 0.5 to 0.65, and from 0.4 to 0.55 for the anomalies. Our validation data set includes both bogs (rain-fed) and fens (ground and/or surface water influence) and reveals that the metrics improved less for fens. In addition, a comparison of evapotranspiration and soil moisture estimates over peatlands will be presented, albeit only with limited ground-based validation data. We will discuss strengths and weaknesses of the new model by focusing on time series of specific validation sites.

Bechtold, M.↗

The Quick Fire Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4: Technical Report Series on Global Modeling and Data Assimilation - Volume 38

Biomass burning is an important source of particulates and trace gases and a major element of the terrestrial carbon cycle. Well constrained emissions from vegetation fires are needed to model direct and indirect effects of biomass burning aerosols, to model homogeneous and heterogeneous chemistry in the atmosphere, and to perform credible Earth system analysis, and climate and air pollution studies. To improve the performance of NASA Goddard Earth Observing System Model (GEOS) in the areas of atmospheric constituent modeling with a focus on biomass burning we developed the Quick Fire Emissions Dataset (QFED). The QFED emissions are based on the fire radiative power (top-down) approach and draw on the cloud correction method developed in the Global Fire Assimilation System (GFAS). Location and fire radiative power of fires are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 fire products (MOD14 and MYD14) and the MODIS Geolocation products (MOD03 and MYD03). QFED strengths are high spatial and temporal resolutions and near-real time availability. Daily mean emissions are available at 0.3125 times 0.25 degrees and in recent versions also at 0.1 times 0.1 degrees. QFED provides emissions of black carbon, organic carbon, sulfur dioxide, carbon monoxide, carbon dioxide, PM2.5, ammonia, nitrogen oxides, methyl ethyl ketone, propylene, ethane, propane, n- and i-butane, acetaldehyde, formaldehyde, acetone and methane. Two QFED product systems are maintained by the NASA Global Modeling and Assimilation Office (GMAO): one that produces near real-time daily emissions used operationally in the GEOS-5 Data Assimilation System, and one that produces an extended historical dataset with daily emissions from March 2000 to the present. The historical dataset also provides monthly mean and monthly climatological emissions.

Koster, Randal D.↗

Assimilation of GMI and ATMS Observations in the Rainbands of Hurricanes

We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of tropical cyclones (TC). These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated into numerical models to provide accurate initial conditions for the NWP models. The BMCI technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI). The retrieved profiles are then assimilated into Hurricane WRF using GSI data assimilation system. This talk presents the development of the BMCI retrieval system as well as the preliminary results of assimilating the retrievals into NASA GEOS-5 model.

Moradi, Isaac↗

Impacts of Snow Darkening Effects by Light Absorbing Aerosols on Hydroclimate of Eurasia During Boreal Spring and Summer

In this study, we used the NASA GEOS-5 climate model to investigate the impact of snow darkening by light-absorbing aerosols on the hydro-climate if Eurasia during boreal spring and summer. Two sets of 10-member ensemble model integrations with prescribed sea surface temperature were carried out for 10 simulated years (2002-2011); one includes snow-darkening effects (SDE) by light-absorbing aerosols, and one does not (NSDE). Differences between the two experiments in the hydro-climates over Eurasia were evaluated. Results show that SDE warming is most pronounced during the melting season due to strong snow-albedo feedback in the vicinity of the retreating seasonal snowline. SDE spurs a Wet-First-Dry-Later (WFDL) modulation of the surface energy and water balances, characterized by an accelerated (days-to-weeks) snowmelt along with excessive runoff and a warming and wetting of the land during the early part of the melting season. The fast snowmelt is followed by a prolonged (> months) warming and desiccation of the land, stemming from a deficit of late-season snowmelt water and increased surface evaporation. The prolonged warming and drying of the land atmosphere system persist through the summer, sustained by atmospheric conditions favorable for the development of atmospheric blocking, i.e., higher mid-to-upper tropospheric geopotential height, lower relative humidity, reduced cloudiness and enhanced atmospheric subsidence. Overall, SDE by light absorbing aerosols leads to a warmer and drier boreal summer hydro-climate, increasing the frequency of the top 5% and 1% extreme hot days (as defined by NSDE statistics) over western and northern Eurasia by approximately three-fold and tenfold, respectively.

Snow darkening↗

The Central Role of Air Quality Observations in NASA's GEOS Composition Forecasting Model

The NASA GEOS composition forecast model (GEOS-CF) provides global, high-resolution (25 km) air quality forecasts in near-real time. This system combines the operational GEOS-5 weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to provide detailed chemical analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). The resolution of the forecasts is the highest compared to current, publicly-available global composition forecasts.Air quality observations are an indispensable tool to evaluate the model's ability to capture the strong temporal and spatial gradients of air pollutants across the globe. We show how comparisons against near-real time observations available through OpenAQ (www.openaq.org) demonstrate the model's overall success in reproducing surface concentrations of ozone, nitrogen dioxide, and PM2.5. This analysis also helps identifying current limitations of the model, for example over South America. The model-observation mismatches are most likely caused by uncertainties in the emissions data. Using the example of Rio de Janeiro, we show how the model skill can be improved by using local, high-resolution emission inventories in combination with air quality data.

Keller, Christoph A.↗

Planetary Boundary Layer Height from AIRS and MERRA-2 Products at NASA GES DISC, and Insights from Data Intercomparison

The Atmospheric Infrared Sounder (AIRS) is the hyperspectral infrared sounder onboard NASA's Aqua satellite, launched in 2002. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), in collaboration with NASA Sounder Team at JPL, provides processing, archiving, and distribution services for NASA sounders: the Aqua AIRS mission and the subsequent Suomi-National Polar-orbiting Partnership Cross-track Infrared Sounder (CrIS) mission. The Planetary Boundary Layer (PBL) Height is a new variable added in the AIRS Version 6 support product. It is derived based on gradients of the retrieved atmospheric thermodynamic profile, and gives the pressure at the top of PBL over the ocean. The GES DISC also provides services for the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product generated by the Goddard Earth Observing System Model, Version 5 (GEOS-5) data assimilation system. The monthly PBL Height variable has been available in the Giovanni system, which is a Web-based application developed by the GES DISC providing a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. In this work, we will present the monthly PBL Height data from AIRS and MERRA-2 and the services to support data intercomparison, such as access, plotting, subsetting, re-gridding, and generation of a multi-year monthly mean. We will also show intercomparison results, and evaluate whether (over the ocean) AIRS can observe PBL features similar to the reanalysis product at monthly and longer-term scales.

AIRS↗

Soil Moisture Active Passive (SMAP) Mission Level 4 Surface and Root Zone Soil Moisture (L4_SM) Product Specification Document

This is the Product Specification Document (PSD) for Level 4 Surface and Root Zone Soil Moisture (L4_SM) data for the Science Data System (SDS) of the Soil Moisture Active Passive (SMAP) project. The L4_SM data product provides estimates of land surface conditions based on the assimilation of SMAP observations into a customized version of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) land data assimilation system (LDAS). This document applies to any standard L4_SM data product generated by the SMAP Project.

Soil Moisture↗

A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments

In the near future, there will be several new instruments measuring atmospheric composition from geostationary orbit over North America, East Asia, and Europe. This constellation of satellites will provide high resolution, time resolved measurements of trace gases and aerosols for monitoring air quality and tracking pollution sources. This paper describes a detailed, fast, and accurate (less than 1.0% uncertainty) method for calculating synthetic top of the atmosphere (TOA) radiances from a global simulation with a mesoscale free running model, the GEOS-5 Nature Run, for remote sensing instruments in geostationary orbit that measure in the ultraviolet-visible spectral range (UV-Vis). Generating these synthetic observations is the first step of an Observing System Simulation Experiment (OSSE), a framework for evaluating the impact of a new observation or algorithm. This paper provides details of the model sampling, aerosol and cloud optical properties, surface reflectance modeling, Rayleigh scattering calculations, and a discussion of the uncertainties of the simulated TOA radiance. An application for the simulated TOA radiance observations is demonstrated in the manuscript. Simulated TEMPO (Tropospheric Emissions: Monitoring of Pollution) and GOES-R (Geostationary Operational Environmental Satellites) observations were used to show how observations from the two instruments could be combined to facilitate aerosol type discrimination. The results demonstrate the viability of a detailed instrument simulator for radiance measurements in the UV-Vis that is capable of accurately simulating high resolution, time-resolved measurements with reasonable computational efficiency.

Castellanos, Patricia↗

The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is NASA’s latest reanalysis for the satellite era (1980-present) using the Goddard Earth Observing System version 5 (GEOS-5) Earth system model. MERRA-2 provides several improvements over its predecessor (MERRA), including the inclusion of interactive aerosols for the entire period. In addition to assimilating bias-corrected Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, it also includes the assimilation of bias-corrected AOD from Advanced Very High Resolution Spectroradiometer (AVHRR) instruments, Multi-angle Imaging SpectroRadiometer (MISR) AOD over bright surfaces, and ground-based Aerosol Robotic Network (AERONET) AOD. This paper is the second of a pair that summarizes our efforts to assess the quality of the MERRA-2 aerosol assimilation. In this study, we first follow previous work performed with version 1 of the MERRA Aerosol Reanalysis (MERRAero) using independent observations. The evaluation of MERRA-2 Absorption Aerosol Optical Depth (AAOD) and ultra-violet Aerosol Index (UV-AI) against the Ozone Monitoring Instrument (OMI) observations show good agreement, particularly over dusty regions where our previous efforts improved model aerosol optical properties. Next, we find that aerosol assimilation system improves the aerosol vertical structure when compared to estimates from the same version of the model without AOD assimilation. A similar conclusion is found for MERRA-2 aerosol surface fine particulate matter (PM (sub 2.5)). However, deficiencies in the forward model such as missing emissions noted during the MERRAero study still explain the MERRA-2 PM(sub 2.5) bias relative to observations over the United States. Finally, to illustrate successes and weaknesses of the AOD assimilation, we focus on the performance of the MERRA-2 aerosol system during several major aerosol events: the Mount Pinatubo eruption in 1991, a Saharan dust event and transportation over the Atlantic Ocean during April 2010, the Rim Fire of summer 2013 in California, and an extreme pollution event over China in January 2013. We conclude with a summary that points to best practices for utilizing the MERRA-2 aerosol reanalysis in future studies.

V Buchard↗

Lidar-Polarimeter Retrieval OSSEs in Support of NASA's Aerosols and Clouds-Convection-Precipitation (ACCP) Study

The 2017 Decadal Survey (DS) highlighted Earth System Science themes, science and application questions, and several high priority objectives that have led to the inclusion of Aerosols (A) and Clouds-Convection-Precipitation (CCP) as Designated Observables (DOs). On June 1, 2018, several NASA centers (GSFC, LaRC, JPL, MSFC, GRC and ARC) submitted a joint Study Plan to the NASA Earth Science Division for the Aerosol (A) and Cloud, Convection, and Precipitation (CCP) Pre-formulation Study (A-CCP). The DS and the A-CCP team recognized the science merit in combining the A and CCP DOs for both enhancing the ability to address a number of science objectives and also to provide an expanded capability to address additional objectives beyond those addressed by individual DOs.A critical element of the A-CCP observing strategy is to make extensive use of new passive and active sensors as well as of the so-called Program-of-Record (PoR), complemented by a fully integrated sub-orbital component. Central to this observing system design is the adoption of a Value Framework in which quantitative assessment of the science benefits of space-and air-borne assets is a key element. Given pre-defined A-CCP science objectives and geophysical variables with desired accuracies, A-CCP relies on a spectrum of Observing System Simulation Experiments (OSSEs) aimed at addressing pixel level retrieval uncertainties and sampling trade-offs. In this talk we will discuss a subset of Retrieval OSSEs being considered for A-CCP, namely, synergistic lidar-polarimeter retrievals of particular relevance for the A-CCP aerosol science objectives. Starting with aerosol states from the GEOS-5 Nature Run (G5NR) sampled along specific satellite orbits, we simulate polarized radiances at the desired polarimeter wavelengths with the Vector Linearized Direct Ordinate Radiative Transfer (VLIDORT) model, alongside the lidar signal for the relevant lidars with realistic error characterization. Next, inversions are performed with the Generalized Retrieval of Aerosol and Surface Properties (GRASP) system and the accuracy of the retrieved geophysical variables are assessed. In this presentation we will highlight results for key architectures being considered for A-CCP.

da Silva, Arlindo↗

Sea Ice Outlook for September 2015 June Report - NASA Global Modeling and Assimilation Office

The recent decline in perennial sea ice cover in Arctic Ocean is a topic of enormous scientific interest and has relevance to a broad variety of scientific disciplines and human endeavors including biological and physical oceanography, atmospheric circulation, high latitude ecology, the sustainability of indigenous communities, commerce, and resource exploration. A credible seasonal prediction of sea ice extent would be of substantial use to many of the stakeholders in these fields and may also reveal details on the physical processes that result in the current trends in the ice cover. Forecasts are challenging due in part to limitations in the polar observing network, the large variability in the climate system, and an incomplete knowledge of the significant processes. Nevertheless it is a useful to understand the current capabilities of high latitude seasonal forecasting and identify areas where such forecasts may be improved. Since 2008 the Arctic Research Consortium of the United States (ARCUS) has conducted a seasonal forecasting contest in which the average Arctic sea ice extent for the month of September (the month of the annual extent minimum) is predicted from available forecasts in early June, July, and August. The competition is known as the Sea Ice Outlook (SIO) but recently came under the auspices of the Sea Ice Prediction Network (SIPN), and multi-agency funded project to evaluate the SIO. The forecasts are submitted based on modeling, statistical, and heuristic methods. Forecasts of Arctic sea ice extent from the GMAO are derived from seasonal prediction system of the NASA Goddard Earth Observing System model, version 5 (GEOS 5) coupled atmosphere and ocean general circulation model (AOGCM). The projections are made in order to understand the relative skill of the forecasting system and to determine the effects of future improvements to the system. This years prediction is for a September average Arctic ice extent of 5.030.41 million km2.

SIPN↗

Evaluation of MERRAero (MERRA Aerosol Reanalysis)

This presentation focuses on MERRA Aerosol Reanalysis (MERRAero) which is the first aerosol reanalysis produced at GMAO. This presentation involve an overview of MERRAero. The evaluation of MERRAero absorption and the evaluation of MERRAero Surface PM 2.5 will also be discussed.

Aerosol↗