SEARCH · Engineering Papers

Results for “forecast error”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20

All sources NASA NTRS DOE OSTI

NASA NTRS2008

Data Assimilation Experiments using Quality Controlled AIRS Version 5 Temperature Soundings

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AlRS data. Version 5 contains accurate case-by-case error estimates for most derived products, which are also used for quality control. We have conducted forecast impact experiments assimilating AlRS quality controlled temperature profiles using the NASA GEOS-5 data assimilation system, consisting of the NCEP GSI analysis coupled with the NASA FVGCM. Assimilation of quality controlled temperature profiles resulted in significantly improved forecast skill in both the Northern Hemisphere and Southern Hemisphere Extra-Tropics, compared to that obtained from analyses obtained when all data used operationally by NCEP except for AlRS data is assimilated. Experiments using different Quality Control thresholds for assimilation of AlRS temperature retrievals showed that a medium quality control threshold performed better than a tighter threshold, which provided better overall sounding accuracy; or a looser threshold, which provided better spatial coverage of accepted soundings. We are conducting more experiments to further optimize this balance of spatial coverage and sounding accuracy from the data assimilation perspective. In all cases, temperature soundings were assimilated well below cloud level in partially cloudy cases. The positive impact of assimilating AlRS derived atmospheric temperatures all but vanished when only AIRS stratospheric temperatures were assimilated. Forecast skill resulting from assimilation of AlRS radiances uncontaminated by clouds, instead of AlRS temperature soundings, was only slightly better than that resulting from assimilation of only stratospheric AlRS temperatures. This reduction in forecast skill is most likely the result of significant loss of tropospheric information when only AIRS radiances unaffected by clouds are used in the data assimilation process.

Susskind, Joel↗

NASA NTRS2017

Description of the GMAO OSSE for Weather Analysis Software Package: Version 3

The Global Modeling and Assimilation Office (GMAO) at the NASA Goddard Space Flight Center has developed software and products for conducting observing system simulation experiments (OSSEs) for weather analysis applications. Such applications include estimations of potential effects of new observing instruments or data assimilation techniques on improving weather analysis and forecasts. The GMAO software creates simulated observations from nature run (NR) data sets and adds simulated errors to those observations. The algorithms employed are much more sophisticated, adding a much greater degree of realism, compared with OSSE systems currently available elsewhere. The algorithms employed, software designs, and validation procedures are described in this document. Instructions for using the software are also provided.

OSSE↗

NASA NTRSDate not supplied

Improving the Representation of Land Surface Processes using the Data Assimilation Research Testbed (DART)

The land surface is a critical part of the earth system as processes related to water, carbon, energy and nitrogen cycling have important implications for climate forcing, air quality, water availability and seasonal atmospheric forecasting. Despite advances in land surface modeling, land surface model performance is often limited because of errors related to initial and boundary conditions, model structure, and parameters. Data assimilation (DA) techniques combined with an expanding network of earth system observations present an opportunity to reduce these errors and improve simulations. Here we apply an Ensemble Kalman Filter DA system as part of the Data Assimilation Research Testbed to a variety of land surface simulations. First, we describe the use of remotely sensed biomass observations to provide improved simulations of plant phenology, carbon and water cycling for regions highly sensitive to climate change (Western US, China, and Arctic). We discuss approaches to account for systemic biases between models and observations, including the use of spatially-varying adaptive ensemble inflation as an alternative approach to re-scaling soil moisture observations. Finally, we discuss a strategy to incorporate complementary observations (snow water equivalent, solar-induced fluorescence) to better constrain the representation of carbon and water cycling across complex terrain.

DART↗

NASA NTRSDate not supplied

Improving the Representation of Land Surface Processes Using the Data Assimilation Research Testbed (DART)

The land surface is a critical part of the earth system as processes related to water, carbon, energy and nitrogen cycling have important implications for climate forcing, air quality, water availability and seasonal atmospheric forecasting. Despite advances in land surface modeling, land surface model performance is often limited because of errors related to initial and boundary conditions, model structure, and parameters. Data assimilation (DA) techniques combined with an expanding network of earth system observations present an opportunity to reduce these errors and improve simulations. Here, we emphasize the implementation of tools and approaches to overcome challenges related to land DA to constrain carbon and water cycling. In particular, we discuss the implementation of adaptive inflation to modify ensemble spread in response to time-varying networks of gridded observations. We also discuss methods to generate ensemble spread through boundary condition (meteorology) forcing that can be applied to site-level applications. Next, we describe the application of vertical localization upon surface soil moisture observations, and forward operators specifically designed for the assimilation of snow and solar-induced fluorescence observations. Finally, we discuss the potential benefit of a quantile conserving filter used to update bounded quantities (state or parameter values).

Brett Raczka↗

NASA NTRS1984

An objective method for forecasting tropical cyclone motion using Nimbus and NOAA-2 infrared measurements

A statistical method for predicting tropical cyclone positions 12, 24, 48 and 72 hours after initial observation is described. Satellite measurements were at the 11.0 micron channel and at equivalent blackbody temperatures which give representations of the structure of the cyclones, as well as the circulation features surrounding them. In all, 197 individual measurements for blackbody temperature were made. Algorithms prepared from a single satellite image included 14 climatological and persistence type variables, and are based on eigenvector preprocessing and a series of independent screening tests. The independent tests showed that the average error made by the algorithms from the single satellite observation were comparable to the 48 hr Joint Typhoon Warning Center (JTWC) forecast and were about 10 percent better for 72 hr forecasts. Forecasts using only climatological persistence variables were found to be 20 percent worse than JTWC for 24 hr forecasts and 10 percent worse for 48 and 72 hr forecasts. When both satellite and nonsatellite variables were included, it was found that the method was comparable to JTWC for the 24 and 48 hr forecasts and about 25 percent better for the 72 hr forecasts.

Hunter, H. E.↗

NASA NTRS2011

MesoNAM Verification Phase II

The 45th Weather Squadron Launch Weather Officers use the 12-km resolution North American Mesoscale model (MesoNAM) forecasts to support launch weather operations. In Phase I, the performance of the model at KSC/CCAFS was measured objectively by conducting a detailed statistical analysis of model output compared to observed values. The objective analysis compared the MesoNAM forecast winds, temperature, and dew point to the observed values from the sensors in the KSC/CCAFS wind tower network. In Phase II, the AMU modified the current tool by adding an additional 15 months of model output to the database and recalculating the verification statistics. The bias, standard deviation of bias, Root Mean Square Error, and Hypothesis test for bias were calculated to verify the performance of the model. The results indicated that the accuracy decreased as the forecast progressed, there was a diurnal signal in temperature with a cool bias during the late night and a warm bias during the afternoon, and there was a diurnal signal in dewpoint temperature with a low bias during the afternoon and a high bias during the late night.

Watson, Leela R.↗

NASA NTRS1990

Space-based Doppler lidar sampling strategies: Algorithm development and simulated observation experiments

Lidar Atmospheric Wind Sounder (LAWS) Simulation Models (LSM) were developed to evaluate the potential impact of global wind observations on the basic understanding of the Earth's atmosphere and on the predictive skills of current forecast models (GCM and regional scale). Fully integrated top to bottom LAWS Simulation Models for global and regional scale simulations were developed. The algorithm development incorporated the effects of aerosols, water vapor, clouds, terrain, and atmospheric turbulence into the models. Other additions include a new satellite orbiter, signal processor, line of sight uncertainty model, new Multi-Paired Algorithm and wind error analysis code. An atmospheric wind field library containing control fields, meteorological fields, phenomena fields, and new European Center for Medium Range Weather Forecasting (ECMWF) data was also added. The LSM was used to address some key LAWS issues and trades such as accuracy and interpretation of LAWS information, data density, signal strength, cloud obscuration, and temporal data resolution.

Emmitt, G. D.↗

NASA NTRS2024

Observing System Simulation Experiments Exploring Potential Spaceborne Deployment Options for a Differential Absorption Radar Measuring Marine Surface Pressures

A new technology for remote measurements of marine surface pressure has been proposed, employing a V-band differential absorption radar and a radiometric temperature sounder to calculate the total column atmospheric mass. Observing System Simulation Experiments (OSSEs) are performed to evaluate the potential impact of Spaceborne Marine Surface Pressure (SMSP) on Numerical Weather Prediction. These experiments build on prior efforts (Privé, McLinden, et al., 2023, https://doi.org/10.16993/tellusa.3254), but with an updated version of the OSSE framework and with more sophisticated simulation of the SMSP observations and a longer experiment period. Several different instrument configurations are compared, including both scanning and non-scanning orbits. SMSP impacts are calculated for analysis quality and forecast skill, and a forecast sensitivity observation impact tool is employed to place SMSP observations in context with the global observing network. The effects of rain contamination on observation quality are explored. Different magnitudes of simulated SMSP observation error are tested in the context of data assimilation to show the range of potential behaviors. Overall, SMSP observations are found to be most beneficial in the southern hemisphere extratropics, with statistically significant forecast improvements for the first 72 hr of the forecast. A constellation of four non-scanning SMSP satellites is found to outperform a single scanning instrument with a 250 km wide swath.

N C Prive↗

NASA NTRS1983

The Snowmelt-Runoff Model (SRM) user's manual

A manual to provide a means by which a user may apply the snowmelt runoff model (SRM) unaided is presented. Model structure, conditions of application, and data requirements, including remote sensing, are described. Guidance is given for determining various model variables and parameters. Possible sources of error are discussed and conversion of snowmelt runoff model (SRM) from the simulation mode to the operational forecasting mode is explained. A computer program is presented for running SRM is easily adaptable to most systems used by water resources agencies.

Martinec, J.↗

NASA NTRS1984

Analogs in the wintertime 500 mb height field

Lorenz (1969) has considered the possibility that an atmospheric circulation pattern might recur, taking into account the definition of a pair of analogous flows or 'analogs'. The present investigation is concerned with a search of time series of Northern Hemisphere wintertime geopotential heights for possible analogs. Attention is given to data and analysis procedure, analog quality, and error growth rates. The obtained results imply that the found analogs are not a useful tool for forecasting the evolution of the midlatitude flow on time scales of several days. It is pointed out, however, that the negative results should not be extended to other applications of analog forecasting.

Gutzler, D. S.↗

NASA NTRS1995

Use of an adjoint model for finding triggers for Alpine lee cyclogenesis

The authors propose a new procedure, designated the adjoint-based genesis diagnostic (AGD) procedure, for studying triggering mechanisms and the subsequent genesis of the synoptic phenomena of interest. This procedure makes use of a numerical model sensitivity to initial conditions and the nonlinear evolution of the initial perturbations that are designed using this sensitivity. The model sensitivity is evaluated using the associated adjoint model. This study uses the dry version of the National Center for Atmospheric Research Mesoscale Adjoint Modeling System (MAMS) for the numerical experiments. The authors apply the AGD procedure to two cases of Alpine lee cyclogenesis that were observed during the Alpine Experiment special observations period. The results show that the sensitivity fields that are produced by the adjoint model and the associated initial perturbations are readily related to the probable triggering mechanisms for these cyclones. Additionally, the nonlinear evolution of these initial perturbations points toward the physical processes involved in the lee cyclone formation. The AGD experiments for a weak cyclone case indicate that the MAMS forecast model has an underrepresented topographic forcing due to the sigma vertical coordinate and that this model error can be compensated by adjustments in the initial conditions that are related to the triggering mechanisms, which is not associated with the topographic blocking mechanism.

Vukicevic, Tomislava↗

NASA NTRSDate not supplied

Insights Into the Hydrology of the Congo Peatlands Through Land Surface Modeling and Data Assimilation

The 16.8 million ha of peatlands in the Cuvette Centrale wetland complex in the Congo Basin is one of the largest peatland regions on Earth but still highly understudied. Understanding the hydrological functioning of these peatlands and the effects of external disturbances thereon remains a major challenge. Recent research suggested fundamental hydrological differences between the Congo peatlands and the well-studied Southeast Asian peatlands. The Congo peatlands have a doming gradient that is up to ten times smaller, and they are influenced by river hydrology to some extent. In this study, we explore the Congo peatland hydrology through land surface modeling and data assimilation. We build upon our recently developed tropical PEATCLSM module (Apers et al., 2022) that was parameterized based on data from Southeast Asian peatlands due to the lack of field data from other tropical peatland regions. In a first step, we derive Congo-specific peat hydraulic and discharge function parameters from a scalar parametrization of water level dynamics in the Congo peatlands, using observed water level data at two locations. These Congo-specific parameters differ considerably from the original literature-based parameters from Southeast Asian peatlands. In a second step, we apply our original and Congo-specific parameters in an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture and Ocean Salinity (SMOS) mission. The data assimilation results are used in two ways. First, the effect of these parameters on the simulated peatland hydrology and the observation-minus-forecast Tb residuals is evaluated. It is hypothesized that the new parameters reduce the previously reported modeling errors over the Congo peatlands and reduce the residuals in Tb as well. Second, we analyze the data assimilation diagnostics to learn about other model improvement possibilities. In preliminary results, we found long periods of temporally autocorrelated total water storage increments (difference of pre- and post-update) that coincided with anomalies in river stages measured upstream of the peatlands. Since PEATLCSM neglects possible river influence, this concurrence suggests that the typically used grid-based approach of land surface models should be combined with a river routing scheme over the Congo peatlands.

Sebastian Apers↗

NASA NTRS2000

Evaluation of the Regional Atmospheric Modeling System in the Eastern Range Dispersion Assessment System

The Applied Meteorology Unit is conducting an evaluation of the Regional Atmospheric Modeling System (RAMS) contained within the Eastern Range Dispersion Assessment System (ERDAS). ERDAS provides emergency response guidance for operations at the Cape Canaveral Air Force Station and the Kennedy Space Center in the event of an accidental hazardous material release or aborted vehicle launch. The prognostic data from RAMS is available to ERDAS for display and is used to initialize the 45th Range Safety (45 SW/SE) dispersion model. Thus, the accuracy of the 45 SW/SE dispersion model is dependent upon the accuracy of RAMS forecasts. The RAMS evaluation task consists of an objective and subjective component for the Florida warm and cool seasons of 1999-2000. The objective evaluation includes gridded and point error statistics at surface and upper-level observational sites, a comparison of the model errors to a coarser grid configuration of RAMS, and a benchmark of RAMS against the widely accepted Eta model. The warm-season subjective evaluation involves a verification of the onset and movement of the Florida east coast sea breeze and RAMS forecast precipitation. This interim report provides a summary of the RAMS objective and subjective evaluation for the 1999 Florida warm season only.

Case, Jonathan↗

NASA NTRS2010

Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best on the average from the perspective of improving Global 7 day forecast skill.

Susskind, Joel↗

NASA NTRS2010

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Weather Forecasting

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimension variational (3DVAR) analysis component (WRF-Var). Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in both clear and partly cloudy regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts due to instability added in the forecast soundings by the AIRS profiles. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

Chou, Shih-Hung↗

NASA NTRS1978

Experiments in monthly mean simulation of the atmosphere with a coarse-mesh general circulation model

The Hansen atmospheric model was used to compute five monthly forecasts (October 1976 through February 1977). The comparison is based on an energetics analysis, meridional and vertical profiles, error statistics, and prognostic and observed mean maps. The monthly mean model simulations suffer from several defects. There is, in general, no skill in the simulation of the monthly mean sea-level pressure field, and only marginal skill is indicated for the 850 mb temperatures and 500 mb heights. The coarse-mesh model appears to generate a less satisfactory monthly mean simulation than the finer mesh GISS model.

Lutz, R. J.↗

NASA NTRS1982

Evaluation of Thompson-type trend and monthly weather data models for corn yields in Iowa, Illinois, and Indiana

An evaluation was made of Thompson-Type models which use trend terms (as a surrogate for technology), meteorological variables based on monthly average temperature, and total precipitation to forecast and estimate corn yields in Iowa, Illinois, and Indiana. Pooled and unpooled Thompson-type models were compared. Neither was found to be consistently superior to the other. Yield reliability indicators show that the models are of limited use for large area yield estimation. The models are objective and consistent with scientific knowledge. Timely yield forecasts and estimates can be made during the growing season by using normals or long range weather forecasts. The models are not costly to operate and are easy to use and understand. The model standard errors of prediction do not provide a useful current measure of modeled yield reliability.

French, V.↗

NASA NTRS2001

Uncertainty Optimization Applied to the Monte Carlo Analysis of Planetary Entry Trajectories

Recently, strong evidence of liquid water under the surface of Mars and a meteorite that might contain ancient microbes have renewed interest in Mars exploration. With this renewed interest, NASA plans to send spacecraft to Mars approx. every 26 months. These future spacecraft will return higher-resolution images, make precision landings, engage in longer-ranging surface maneuvers, and even return Martian soil and rock samples to Earth. Future robotic missions and any human missions to Mars will require precise entries to ensure safe landings near science objective and pre-employed assets. Potential sources of water and other interesting geographic features are often located near hazards, such as within craters or along canyon walls. In order for more accurate landings to be made, spacecraft entering the Martian atmosphere need to use lift to actively control the entry. This active guidance results in much smaller landing footprints. Planning for these missions will depend heavily on Monte Carlo analysis. Monte Carlo trajectory simulations have been used with a high degree of success in recent planetary exploration missions. These analyses ascertain the impact of off-nominal conditions during a flight and account for uncertainty. Uncertainties generally stem from limitations in manufacturing tolerances, measurement capabilities, analysis accuracies, and environmental unknowns. Thousands of off-nominal trajectories are simulated by randomly dispersing uncertainty variables and collecting statistics on forecast variables. The dependability of Monte Carlo forecasts, however, is limited by the accuracy and completeness of the assumed uncertainties. This is because Monte Carlo analysis is a forward driven problem; beginning with the input uncertainties and proceeding to the forecasts outputs. It lacks a mechanism to affect or alter the uncertainties based on the forecast results. If the results are unacceptable, the current practice is to use an iterative, trial-and-error approach to reconcile discrepancies. Therefore, an improvement to the Monte Carlo analysis is needed that will allow the problem to be worked in reverse. In this way, the largest allowable dispersions that achieve the required mission objectives can be determined quantitatively.

Olds, John↗