5,767 research outputs found

    Adaptation of NEMO-LIM3 model for multigrid high resolution Arctic simulation

    Full text link
    High-resolution regional hindcasting of ocean and sea ice plays an important role in the assessment of shipping and operational risks in the Arctic Ocean. The ice-ocean model NEMO-LIM3 was modified to improve its simulation quality for appropriate spatio-temporal resolutions. A multigrid model setup with connected coarse- (14 km) and fine-resolution (5 km) model configurations was devised. These two configurations were implemented and run separately. The resulting computational cost was lower when compared to that of the built-in AGRIF nesting system. Ice and tracer boundary-condition schemes were modified to achieve the correct interaction between coarse- and fine grids through a long ice-covered open boundary. An ice-restoring scheme was implemented to reduce spin-up time. The NEMO-LIM3 configuration described in this article provides more flexible and customisable tools for high-resolution regional Arctic simulations

    A Study of Land Surface Processes Using Land Surface Models Over the Little River Experimental Watershed

    Get PDF
    Three different land surface models (Hydrological improvements to the Simplified version of the Simple Biosphere model (HySSiB), Noah model, and Community Land Model (CLM)) were simulated on the NASA Goddard Space Flight Center’s Land Information System platform at 1-km resolution over the Little River Experimental Watershed, Georgia, and the simulated results were analyzed to address the local-scale land-atmosphere processes. All the three models simulated the soil moisture in space and time realistically. The Noah model produced higher soil moisture whereas the CLM got lower soil moisture with many dry down phases. CLM and HySSiB models were oversensitive to the atmospheric events. Different vertical discretizations of the model layers affected the soil moisture results in all the three models. The arithmetic model ensemble mean soil moisture performed reasonably well even at individual in-situ measurement sites. We found that different model schemes partitioned the incoming water and energy differently and hence produced different results for the water and energy budget parameters. In CLM, the energy and water budget parameters were very closely connected to the soil moisture (e.g., evaporation, latent, and sensible heat) change. HySSiB produced very high surface runoff and very low subsurface runoff. The Noah model did not produce much surface and subsurface runoff resulting in high surface soil moisture. We did not find much variability in Noah latent heat, sensible heat, and ground heat fluxes. From soil moisture data assimilation point of view, the mean bias removed Noah soil moisture was found to be better than other data sets

    On the Predictability of Hub Height Winds

    Get PDF
    Wind energy is a major source of power in over 70 countries across the world, and the worldwide share of wind energy in electricity consumption is growing. The introduction of signicant amounts of wind energy into power systems makes accurate wind forecasting a crucial element of modern electrical grids. These systems require forecasts with temporal scales of tens of minutes to a few days in advance at wind farm locations. Traditionally these forecasts predict the wind at turbine hub heights; this information is then converted by transmission system operators and energy companies into predictions of power output at wind farms. Since the power available in the wind is proportional to the wind speed cubed, even small wind forecast errors result in large power prediction errors. Accurate wind forecasts are worth billions of dollars annually; forecast improvements will result in reduced costs to consumers due to better integration of wind power into the power grid and more effcient trading of wind power on energy markets.This thesis is a scientic contribution to the advancement of wind energy forecasting with mesoscale numerical weather prediction models. After an economic and theoretical overview of the importance of wind energy forecasts, this thesis continues with an analysis of wind speed predictions at hub height using the Weather Research and Forecasting (WRF) model. This analysis demonstrates the need for more detailed analyses of wind speeds and it is shown that wind energy forecasting cannot be reduced solely to forecasting winds at hub height. Calculating only the power output from hub height winds can result in erroneous estimates due to the vertical wind shear in the atmospheric boundary layer (PBL). Results show that the accuracy of modeled wind conditions and wind proles in the PBL depends on the PBL scheme adopted and is different under varying atmospheric stability conditions, among other modeling factors. This has important implications for wind energy applications: shallow stable boundary layers can result in excessive wind shear, which is detrimental for wind energy applications. This is particularly relevant with offshore facilities, which represent a significant portion of new wind farms being constructed. Furthermore, a novel aspect to this study is the presentation of a verification methodology that takes into account wind at different heights where turbines operate.The increasing number of wind farm deployments represents a novel and unique data source for improving mesoscale wind forecasts for wind energy applications. These new measurements include nacelle wind speeds and the turbines' angle of rotation into the wind (yaw angles). This thesis continues with an extensive description of this new data set and its challenges in data assimilation, focusing on data from the Horns Rev I wind farm. Since wind farm data are such a dense data set there is need to derive representative information from the measurements, i.e., thin the data. Different thinning strategies and their impact on improving wind forecasts for wind power predictions are investigated with the WRF Four-Dimensional Data Assimilation system. The median of the whole wind farm was found to be the most successful thinning strategy. Nacelle winds and yaw angles are a promising data set to improve wind predictions downstream of a wind farm as well as at the wind farm itself: Their impact lasted up to 5 hours and depends on time of the day, forecast lead time and weather situation

    Sensitivities of Explicit Hail Predictions and Convective Scale Ensemble Forecasting to Microphysics Parameterizations and Ensemble Data Assimilation Configurations

    Get PDF
    The explicit prediction of deep, moist convection is challenging because small model and initial condition errors rapidly grow and degrade forecast skill. Microphysics schemes employed by convection-allowing models represent a substantial source of model error because microphysical processes are poorly understood and simplifying assumptions must be made to make simulations and forecasts computationally practical. Although data assimilation systems decrease initial condition errors, analysis and forecast skill is sensitive to the experiment design. This dissertation evaluates data assimilation and ensemble forecast system performances at convection-allowing/convection-resolving resolutions, when forecast models employ different multi-moment microphysics parameterization schemes, and the data assimilation configurations are varied. We address the related issues through detailed case studies that provide insights on optimizing the configuration of convection-allowing model forecasts. First, high-resolution hail size forecasts are made for a severe hail event on 19 May 2013 using the Advanced Regional Prediction System (ARPS). Forecasts using the National Severe Storms Laboratory (NSSL) variable density rimed ice double-moment microphysics scheme (referred to as NSSL) exhibit more skill than those using the Milbrandt and Yau double-moment (MY2) or triple-moment (MY3) schemes when verified against radar-derived hail size estimates. Although all three schemes predict severe surface hail coverage with moderate to high skill, MY2 and MY3 forecasts overpredict the maximum hail size. The NSSL scheme uses the two variable density rimed ice categories to generate large, dense hail through the wet growth of graupel. Both the MY2 and MY3 schemes predict hail to be smaller above the 0 °C isotherm because the category is primarily composed of small frozen raindrops; in the melting layer the hail quickly grows because the rimed ice accretes excessive water. MY2 and MY3 forecasts predict the largest hail sizes to be smaller when the accretion water is eliminated beneath the 0 °C isotherm. To improve hailstorm forecast initial conditions, CAPS Ensemble Kalman filter (EnKF) analyses are generated for the 8 May 2017 Colorado severe hail event using either the MY2 or the NSSL scheme in the forecast model. The results of the EnKF analyses are evaluated. With each microphysics scheme two experiments are conducted where reflectivity (Z) observations update either (1) only the hydrometeor mixing ratio or (2) all hydrometeor fields. Experiments that update only hydrometeor mixing ratios can create ensemble error covariances that are unreliable which increases analysis error. Despite improving initial condition estimates, experiments that update all hydrometeor fields underestimate surface hail size, which suggests additional constraint from observations is needed during data assimilation. Correlation patterns between observation prior estimates (e.g., Z) and model state variables are evaluated to determine the impact of hail growth assumptions in the MY and NSSL schemes on the forecast error covariances between microphysical and thermodynamic variables. For the MY2 scheme, Z is negatively correlated with updraft intensity because strong updrafts produce abundant, small hail aloft. The NSSL scheme predicts storm updrafts to produce fewer but larger hailstones aloft, which causes Z and updraft intensity to be positively correlated. Hail production processes also alter the background error covariances for in-cloud air temperature and hydrometeor species. This study documents strong sensitivity of ensemble data assimilation results of hailstorms to the parameterization of microphysical processes, and the need to reduce microphysics parameterization uncertainties. To improve data assimilation configurations for potential operational implementation, EnKF data assimilation experiments based on the operational GSI system employed by the Center for Analysis and Prediction of Storms (CAPS) realtime Spring Forecast Experiments are performed, followed by 6-hour forecasts for a mesoscale convective system (MCS) event on 28-29 May 2017. Experiments are run to evaluate the sensitivity of forecast skill to the configurations of the data assimilation system. Configurations examined include the ensemble initialization and covariance inflation as well as radar observation data thinning, covariance localization radii, observation error settings, and data assimilation frequency. Spin-up ensemble forecast surface temperatures are most skilled when the initial ensemble mean is centered upon the most recent NAM analysis, causing forecasts to predict a strong MCS. Experiments that assimilate radar observations every 5 minutes are better at the placement of high Z values near observed storms but exhibit a substantial decrease in forecast skill initially because of widespread spurious convection. Ensembles that assimilate more observations with less thinning of data or use a larger horizontal covariance localization radius for radar data overpredict the coverage of high Z values due to enhanced spurious convection. Both parameters have modestly positive impacts on forecast skill during the first forecast hour that are quickly lost due to the growth of forecast error. Forecast skill is less sensitive to the ensemble spread inflation factors and observation errors tested during this study. These results provide guidance towards optimizing the GSI EnKF system configuration, for this study the data assimilation configuration employed by the 2019 CAPS Spring Forecast Experiment produces the most skilled forecasts while remaining viable for realtime use

    An assessment of air-sea heat fluxes from ocean and coupled reanalyses

    Get PDF
    Sixteen monthly air–sea heat flux products from global ocean/coupled reanalyses are compared over 1993–2009 as part of the Ocean Reanalysis Intercomparison Project (ORA-IP). Objectives include assessing the global heat closure, the consistency of temporal variability, comparison with other flux products, and documenting errors against in situ flux measurements at a number of OceanSITES moorings. The ensemble of 16 ORA-IP flux estimates has a global positive bias over 1993–2009 of 4.2 ± 1.1 W m−2. Residual heat gain (i.e., surface flux + assimilation increments) is reduced to a small positive imbalance (typically, +1–2 W m−2). This compensation between surface fluxes and assimilation increments is concentrated in the upper 100 m. Implied steady meridional heat transports also improve by including assimilation sources, except near the equator. The ensemble spread in surface heat fluxes is dominated by turbulent fluxes (>40 W m−2 over the western boundary currents). The mean seasonal cycle is highly consistent, with variability between products mostly <10 W m−2. The interannual variability has consistent signal-to-noise ratio (~2) throughout the equatorial Pacific, reflecting ENSO variability. Comparisons at tropical buoy sites (10°S–15°N) over 2007–2009 showed too little ocean heat gain (i.e., flux into the ocean) in ORA-IP (up to 1/3 smaller than buoy measurements) primarily due to latent heat flux errors in ORA-IP. Comparisons with the Stratus buoy (20°S, 85°W) over a longer period, 2001–2009, also show the ORA-IP ensemble has 16 W m−2 smaller net heat gain, nearly all of which is due to too much latent cooling caused by differences in surface winds imposed in ORA-IP

    Report of the proceedings of the Colloquium and Workshop on Multiscale Coupled Modeling

    Get PDF
    The Colloquium and Workshop on Multiscale Coupled Modeling was held for the purpose of addressing modeling issues of importance to planning for the Cooperative Multiscale Experiment (CME). The colloquium presentations attempted to assess the current ability of numerical models to accurately simulate the development and evolution of mesoscale cloud and precipitation systems and their cycling of water substance, energy, and trace species. The primary purpose of the workshop was to make specific recommendations for the improvement of mesoscale models prior to the CME, their coupling with cloud, cumulus ensemble, hydrology, air chemistry models, and the observational requirements to initialize and verify these models

    Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Model

    Get PDF
    Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation SCA indicates only the presence or absence of snow, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes both during the snow season and, in some regions, on into the following spring

    Can a “state of the art” chemistry transport model simulate Amazonian tropospheric chemistry?

    Get PDF
    We present an evaluation of a nested high-resolution Goddard Earth Observing System (GEOS)-Chem chemistry transport model simulation of tropospheric chemistry over tropical South America. The model has been constrained with two isoprene emission inventories: (1) the canopy-scale Model of Emissions of Gases and Aerosols from Nature (MEGAN) and (2) a leaf-scale algorithm coupled to the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model, and the model has been run using two different chemical mechanisms that contain alternative treatments of isoprene photo-oxidation. Large differences of up to 100 Tg C yr^(−1) exist between the isoprene emissions predicted by each inventory, with MEGAN emissions generally higher. Based on our simulations we estimate that tropical South America (30–85°W, 14°N–25°S) contributes about 15–35% of total global isoprene emissions. We have quantified the model sensitivity to changes in isoprene emissions, chemistry, boundary layer mixing, and soil NO_x emissions using ground-based and airborne observations. We find GEOS-Chem has difficulty reproducing several observed chemical species; typically hydroxyl concentrations are underestimated, whilst mixing ratios of isoprene and its oxidation products are overestimated. The magnitude of model formaldehyde (HCHO) columns are most sensitive to the choice of chemical mechanism and isoprene emission inventory. We find GEOS-Chem exhibits a significant positive bias (10–100%) when compared with HCHO columns from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI) for the study year 2006. Simulations that use the more detailed chemical mechanism and/or lowest isoprene emissions provide the best agreement to the satellite data, since they result in lower-HCHO columns
    corecore