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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.

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NASA NTRS1982

Methods of sequential estimation for determining initial data in numerical weather prediction

Numerical weather prediction (NWP) is an initial-value problem for a system of nonlinear differential equations, in which initial values are known incompletely and inaccurately. Observational data available at the initial time must therefore be supplemented by data available prior to the initial time, a problem known as meteorological data assimilation. A further complication in NWP is that solutions of the governing equations evolve on two different time scales, a fast one and a slow one, whereas fast scale motions in the atmosphere are not reliably observed. This leads to the so called initialization problem: initial values must be constrained to result in a slowly evolving forecast. The theory of estimation of stochastic dynamic systems provides a natural approach to such problems. For linear stochastic dynamic models, the Kalman-Bucy (KB) sequential filter is the optimal data assimilation method, for linear models, the optimal combined data assimilation-initialization method is a modified version of the KB filter.

Cohn, S. E.↗
NASA NTRS1984

The impact of Seasat scatterometer winds on the Navy's operational model

The question of whether Seasat scatterometer wind data can have a positive impact on an operational numerical weather prediction (NWP) model is examined. Comparisons are made between assimilations and forecasts made with the Navy's operational forecast and analysis system (NOGAPS) when Seasat data are included or excluded. S1 scores and rms differences show that Seasat data had a negligible effect on NWP forecasts made from the Navy's model in both the northern and the southern hemisphere. A global subjective analysis of all of the forecasts shows similar results.

Duffy, D.↗
NASA NTRS1985

An initialization procedure for assimilating geostationary satellite data into numerical weather prediction models

An attempt was made to offset the limitations of GEO satellites for supplying timely initialization data for numerical weather prediction models (NWP). The NWP considered combined an isentropic representation of the free atmosphere with a sigma-coordinate model for the lower 200 mb. A flux form of the predictive equations described vertical transport interactions at the boundary of the two model domains, thereby accounting for the poor vertical temperature and wind field resolution of GEO satellite data. A variational analysis approach was employed to insert low resolution satellite-sensed temperature data at varying rates. The model vertical resolution was limited to that available from the satellite. Test simulations demonstrated that accuracy increases with the frequency of data updates, e.g., every 0.5-1 hr. The tests also showed that extensive cloud cover negates the capabilities of IR sensors and that microwave sensors will be needed for temperature estimations for 500-1000 mb levels.

Gal-Chen, T.↗
NASA NTRS1987

Use of simulated satellite radiances from a mesoscale numerical model to understand kinematic and dynamic processes

Techniques by which mesoscale numerical weather prediction model output and radiative transfer codes are combined to simulate the radiance fields that a given passive temperature/moisture satellite sensor would see if viewing the evolving model atmosphere are introduced. The goals are to diagnose the dynamical atmospheric processes responsible for recurring patterns in observed satellite radiance fields, and to develop techniques to anticipate the ability of satellite sensor systems to depict atmospheric structures and provide information useful for numerical weather prediction (NWP). The concept of linking radiative transfer and dynamical NWP codes is demonstrated with time sequences of simulated radiance imagery in the 24 TIROS vertical sounder channels derived from model integrations for March 6, 1982.

Kalb, Michael↗
NASA NTRS1993

A preliminary C-band scatterometer model function for the ERS-1 AMI instrument

Backscatter cross section (sigma(sub 0)) measurements from the ERS-1 scatterometer are collocated with surface wind velocity data from operational Numerical Weather Prediction (NWP) analyses to yield a fully empirical C band model function. The empirical model does not obey a power law at speeds less than 5 m/s, and upwind/crosswind ratios are small for all incidence angles at low wind speeds and for small incidence angles at all wind speeds. Downwind sigma(sub 0) exceeds upwind values for incidence angles below approximately 28 degrees. The full ERS-1 scatterometer data set has been reprocessed using the NWP model function.

Freilich, M. H.↗
NASA NTRS1994

The value of NDBC observations during March 1993's 'Storm of the Century'

National Data Buoy Center (NDBC) observations from the western Gulf of Mexico Provided several early indications of more rapid cyclogenesis on 12 March 1993 than was forecast by numerical weather prediction (NWP) models. Observations demostrated a closed circulation with gale-force winds shortly after the storm entered the Gulf of Mexico. Pressure measurements at two buoys off the Texas coast were 4-6 hPa lower than the 12-h NWP forecasts, a significant forecast error. Observations from NDBC's moored buoys and Coastal-Marine Automated Network stations revealed that the developing storm was significantly deeper than was indicated on the National Meteorological Center's automated surface analyses. Ocean wave observations reveal some of the steepest waves NDBC has ever measured, indicating phenomenonal wave growth and a high potential for damage to vessels and structures. A warm eddy caused sea surface temperatures (SST) to be several degrees above normal under the track of the storm, creating a strong SST gradient to the north. This provided ample energy and strengthened the baroclinity. NDBC observations showed the eddy to be somewhat larger and warmer than indicated by the most recent National Hurricane Center analysis. This event demonstrates the tremendous value of NDBC marine observations for accurately detecting the occurrence and strength of coastal cyclogenesis events.

Gilhousen, David B.↗
NASA NTRS1995

Impact of geostationary satellite water vapor channel data on weather analysis and forecasting

Preliminary results from NWP impact studies are indicating that upper-tropospheric wind information provided by tracking motions in sequences of geostationary satellite water vapor imagery can positively influence forecasts on regional scales, and possibly on global scales as well. The data are complimentary to cloud-tracked winds by providing data in cloud-free regions, as well as comparable in quality. First results from GOES-8 winds are encouraging, and further efforts and model impacts will be directed towards optimizing these data in numerical weather prediction (NWP). Assuming successful launches of GOES-J and GMS-5 satellites in 1995, high quality and resolution water vapor imagers will be available to provide nearly complete global upper-tropospheric wind coverage.

Velden, Christopher S.↗
NASA NTRS1999

Application of Lidar Winds to Data Assimilation

One of the major applications of space-based doppler wind lidar is to improve atmospheric analyses and numerical weather prediction (NWP). Since the mid 198,0's, Observing System Simulation Experiments (OSSE's) have been conducted in order to evaluate the potential impact of lidar winds on NWP. These experiments have shown tremendous potential for satellite lidar observations to improve atmospheric analyses and forecasts. In addition, the OSSE's are providing an evaluation of trade-offs in lidar design, and are currently being used to define the specific requirements for lidar winds in terms of horizontal and vertical coverage and accuracy. At the meeting the methodology for these experiments and the main results relating to proposed lidars will be presented.

Atlas, Robert↗
NASA NTRS2000

1DVAR Analysis of Temperature and Humidity using GPS Radio Occulation Data

The Global Positioning System (GPS) enables positioning with a very small receiver. The signals transmitted by the GPS satellites are sensitive to the atmosphere and can be used to perform soundings with the radio occultation technique (e.g., Kursinski et al., 1997). The GPS signal can be converted to refractivity N via the Abel transform. The refractivity can then be related to atmospheric pressure P, temperature T, and water vapor partial pressure P (sub w) the GPS measurement, (between 0.5 and 1.5 km), its self-calibration, and it's nearly all-weather capabilities make it a good candidate for use in data assimilation systems (DAS) and numerical weather prediction (NWP). In order to demonstrate its usefulness in a DAS or NWP system, a first step is to assess its impact oil the analysis. A one-dimensional variational off-line analysis (1DVAR), meaning the data are not assimilated 'In the 3D DAS, constitutes a starting approach to which further enhancements can be made. The chosen observable to be analyzed in this study is the refractivity. One way to extract temperature (humidity) from the refractivity, is to assume a humidity (temperature) profile. One variable may then be retrieved without any a priori information. The 1DVAR approach used here resolves the ambiguity problem raised in the interpretation of these data. It enables retrieving these two atmospheric variables at a reasonable computing cost.

Poli, Paul↗
NASA NTRS2002

ENVISAT Land Surface Processes

This is a progress report of the 2nd phase of the project ENVISAT- Land Surface Processes, which has a 3-year scope. In this project, preparative research is carried out aiming at the retrieval of land surface characteristics from the ENVISAT sensors MERIS and AATSR, for assimilation into a system for Numerical Weather Prediction (NWP). Where in the 1st phase a number of first shot experiments were carried out (aiming at gaining experience with the retrievals and data assimilation procedures), the current 2nd phase has put more emphasis on the assessment and improvement of the quality of the retrieved products. The forthcoming phase will be devoted mainly to the data assimilation experiments and the assessment of the added value of the future ENVISAT products for NWP forecast skill. Referring to the retrieval of albedo, leaf area index and atmospheric corrections, preliminary radiative transfer calculations have been carried out that should enable the retrieval of these parameters once AATSR and MERIS data become available. However, much of this work is still to be carried out. An essential part of work in this area is the design and implementation of software that enables an efficient use of MODTRAN(sub 4) radiative transfer code, and during the current project phase familiarization with these new components has been achieved. Significant progress has been made with the retrieval of component temperatures from directional ATSR-images, and the calculation of surface turbulent heat fluxes from these data. The impact of vegetation cover on the retrieved component temperatures appears manageable, and preliminary comparison of foliage temperature to air temperatures were encouraging. The calculation of surface fluxes using the SEBI concept,which includes a detailed model of the surface roughness ratio, appeared to give results that were in reasonable agreement with local measurements with scintillometer devices. The specification of the atmospheric boundary conditions appears a crucial component, and the use of first guess estimates from the RACMO models partially explains the success. Earlier data assimilation experiments with directional surface temperatures have been analysed a bit further and were also compared to results obtained from directly modeling the surface roughness ratio. Results between these calculations and the data assimilation results appeared well comparable, but a full test in which the surface roughness model is allowed to play a free role during the data assimilation process has yet to be carried out. A considerable number of tasks that have yet to be carried out during Phase 3 has been formulated.

vandenHurk, B. J. J. M.↗
NASA NTRS1998

NASA Scatterometer Provides Global Ocean-Surface Wind Fields with More Structures than Numerical Weather Prediction

The major differences between monthly-mean ocean-surface wind fields derived from the observations of the National Aeronautics and Space Administration (NASA) Scatterometer (NSCAT) and produced by the operational numerical weather prediction (NWP) model of the European Center for Medium-Range Weather Forecasts are found in coastal and equatorial regions, where the sharp changes are smoothed over in NWP products.

scatterometer ocean surface wind weather predictio↗
NASA NTRS2005

Estimating moisture transport over oceans using space-based observations

The moisture transport integrated over the depth of the atmosphere (0) is estimated over oceans using satellite data. The transport is the product of the precipitable water and an equivalent velocity (ue), which, by definition, is the depth-averaged wind velocity weighted by humidity. An artificial neural network is employed to construct a relation between the surface wind velocity measured by the spaceborne scatterometer and coincident ue derived using humidity and wind profiles measured by rawinsondes and produced by reanalysis of operational numerical weather prediction (NWP). On the basis of this relation, 0 fields are produced over global tropical and subtropical oceans (40_N- 40_S) at 0.25_ latitude-longitude and twice daily resolutions from August 1999 to December 2003 using surface wind vector from QuikSCAT and precipitable water from the Tropical Rain Measuring Mission. The derived ue were found to capture the major temporal variability when compared with radiosonde measurements. The average error over global oceans, when compared with NWP data, was comparable with the instrument accuracy specification of space-based scatterometers. The global distribution exhibits the known characteristics of, and reveals more detailed variability than in, previous data.

Artificial Neural Network↗
NASA NTRS2009

Simulating the Effects of Irrigation over the U.S. in a Land Surface Model Based on Satellite Derived Agricultural Data

A novel method is introduced for integrating satellite derived irrigation data and high-resolution crop type information into a land surface model (LSM). The objective is to improve the simulation of land surface states and fluxes through better representation of agricultural land use. Ultimately, this scheme could enable numerical weather prediction (NWP) models to capture land-atmosphere feedbacks in managed lands more accurately and thus improve forecast skill. Here we show that application of the new irrigation scheme over the continental US significantly influences the surface water and energy balances by modulating the partitioning of water between the surface and the atmosphere. In our experiment, irrigation caused a 12% increase in evapotranspiration (QLE) and an equivalent reduction in the sensible heat flux (QH) averaged over all irrigated areas in the continental US during the 2003 growing season. Local effects were more extreme: irrigation shifted more than 100 W/m from QH to QLE in many locations in California, eastern Idaho, southern Washington, and southern Colorado during peak crop growth. In these cases, the changes in ground heat flux (QG), net radiation (RNET), evapotranspiration (ET), runoff (R), and soil moisture (SM) were more than 3 W/m(sup 2), 20 W/m(sup 2), 5 mm/day, 0.3 mm/day, and 100 mm, respectively. These results are highly relevant to continental- to global-scale water and energy cycle studies that, to date, have struggled to quantify the effects of agricultural management practices such as irrigation. Based on the results presented here, we expect that better representation of managed lands will lead to improved weather and climate forecasting skill when the new irrigation scheme is incorporated into NWP models such as NOAA's Global Forecast System (GFS).

Ozdogan, Mutlu↗
NASA NTRS2011

Hyperspectral Remote Sensing of Atmosphere and Surface Properties

Atmospheric Infrared Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), and Cross-track Infrared Sounder (CrIS) are all hyper-spectral satellite sensors with thousands of spectral channels. Top of atmospheric radiance spectra measured by these sensors contain high information content on atmospheric, cloud, and surface properties. Exploring high information content contained in these high spectral resolution spectra is a challenging task due to computation e ort involved in modeling thousands of spectral channels. Usually, only very small fractions (4{10 percent) of the available channels are included in physical retrieval systems or numerical weather forecast (NWP) satellite data assimilations. We will describe a method of simultaneously retrieving atmospheric temperature, moisture, cloud, and surface properties using all available spectral channels without sacrificing computational speed. The essence of the method is to convert channel radiance spectra into super-channels by an Empirical Orthogonal Function (EOF) transformation. Because the EOFs are orthogonal to each other, about 100 super-channels are adequate to capture the information content of the radiance spectra. A Principal Component-based Radiative Transfer Model (PCRTM) developed at NASA Langley Research Center is used to calculate both the super-channel magnitudes and derivatives with respect to atmospheric profiles and other properties. There is no need to perform EOF transformations to convert super channels back to spectral space at each iteration step for a one-dimensional variational retrieval or a NWP data assimilation system. The PCRTM forward model is also capable of calculating radiative contributions due to multiple-layer clouds. The multiple scattering effects of the clouds are efficiently parameterized. A physical retrieval algorithm then performs an inversion of atmospheric, cloud, and surface properties in super channel domain directly therefore both reducing the computational need and preserving the information content of the IASI measurements. The inversion algorithm is based on a non-linear Levenberg-Marquardt method with climatology covariance matrices and a priori information as constraints. One advantage of this approach is that it uses all information content from the hyper-spectral data so that the retrieval is less sensitive to instrument noise and eliminates the need for selecting a sub-set of the channels.

Liu, Xu↗
NASA NTRS2010

4-D Cloud Water Content Fields Derived from Operational Satellite Data

In order to improve operational safety and efficiency, the transportation industry, including aviation, has an urgent need for accurate diagnoses and predictions of clouds and associated weather conditions. Adverse weather accounts for 70% of all air traffic delays within the U.S. National Airspace System. The Federal Aviation Administration has determined that as much as two thirds of weather-related delays are potentially avoidable with better weather information and roughly 20% of all aviation accidents are weather related. Thus, it is recognized that an important factor in meeting the goals of the Next Generation Transportation System (NexGen) vision is the improved integration of weather information. The concept of a 4-D weather cube is being developed to address that need by integrating observed and forecasted weather information into a shared 4-D database, providing an integrated and nationally consistent weather picture for a variety of users and to support operational decision support systems. Weather analyses and forecasts derived using Numerical Weather Prediction (NWP) models are a critical tool that forecasters rely on for guidance and also an important element in current and future decision support systems. For example, the Rapid Update Cycle (RUC) and the recently implemented Rapid Refresh (RR) Weather Research and Forecast (WRF) models provide high frequency forecasts and are key elements of the FAA Aviation Weather Research Program. Because clouds play a crucial role in the dynamics and thermodynamics of the atmosphere, they must be adequately accounted for in NWP models. The RUC, for example, cycles at full resolution five cloud microphysical species (cloud water, cloud ice, rain, snow, and graupel) and has the capability of updating these fields from observations. In order to improve the models initial state and subsequent forecasts, cloud top altitude (or temperature, T(sub c)) derived from operational satellite data, surface observations of cloud base altitude, radar reflectivity, and lightning data are used to help build and remove clouds in the models assimilation system. Despite this advance and the many recent advances made in our understanding of cloud physical processes and radiative effects, many problems remain in adequately representing clouds in models. While the assimilation of cloud top information derived from operational satellite data has merit, other information is available that has not yet been exploited. For example, the vertically integrated cloud water content (CWC) or cloud water path (CWP) and cloud geometric thickness (delta Z) are standard products being derived routinely from operational satellite data. These and other cloud products have been validated under a variety of conditions. Since the uncertainties have generally been found to be less than those found in model analyses and forecasts, the satellite products should be suitable for data assimilation, provided an appropriate strategy can be developed that links the satellite-derived cloud parameters with cloud parameters specified in the model. In this paper, we briefly outline such a strategy and describe a methodology to retrieve cloud water content profiles from operational satellite data. Initial results and future plans are presented. It is expected that the direct assimilation of this new product will provide the most accurate depiction of the vertical distribution of cloud water ever produced at the high spatial and temporal resolution needed for short term weather analyses and forecasts.

Smith, William L., Jr.↗
NASA NTRS2011

Evaluating the Impact of Atmospheric Infrared Sounder (AIRS) Data On Convective Forecasts

The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service (NWS) offices. SPoRT provides real-time NASA products and capabilities to its partners to address specific operational forecast challenges. The mission of SPoRT is to transition observations and research capabilities into operations to help improve short-term weather forecasts on a regional scale. Two areas of focus are data assimilation and modeling, which can to help accomplish SPoRT's programmatic goals of transitioning NASA data to operational users. Forecasting convective weather is one challenge that faces operational forecasters. Current numerical weather prediction (NWP) models that operational forecasters use struggle to properly forecast location, timing, intensity and/or mode of convection. Given the proper atmospheric conditions, convection can lead to severe weather. SPoRT's partners in the National Oceanic and Atmospheric Administration (NOAA) have a mission to protect the life and property of American citizens. This mission has been tested as recently as this 2011 severe weather season, which has seen more than 300 fatalities and injuries and total damages exceeding $10 billion. In fact, during the three day period from 25-27 April, 1,265 storms reports (362 tornado reports) were collected making this three day period one of most active in American history. To address the forecast challenge of convective weather, SPoRT produces a real-time NWP model called the SPoRT Weather Research and Forecasting (SPoRT-WRF), which incorporates unique NASA data sets. One of the NASA assets used in this unique model configuration is retrieved profiles from the Atmospheric Infrared Sounder (AIRS).The goal of this project is to determine the impact that these AIRS profiles have on the SPoRT-WRF forecasts by comparing to a current operational model and a control SPoRT-WRF model that does not contain AIRS profiles.

Kozlowski, Danielle↗
NASA NTRS2006

An Operational Configuration of the ARPS Data Analysis System to Initialize WRF in the NM'S Environmental Modeling System

The Weather Research and Forecasting (WRF) model is the next generation community mesoscale model designed to enhance collaboration between the research and operational sectors. The NM'S as a whole has begun a transition toward WRF as the mesoscale model of choice to use as a tool in making local forecasts. Currently, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) are running the Advanced Regional Prediction System (AIRPS) Data Analysis System (ADAS) every 15 minutes over the Florida peninsula to produce high-resolution diagnostics supporting their daily operations. In addition, the NWS MLB and SMG have used ADAS to provide initial conditions for short-range forecasts from the ARPS numerical weather prediction (NWP) model. Both NM'S MLB and SMG have derived great benefit from the maturity of ADAS, and would like to use ADAS for providing initial conditions to WRF. In order to assist in this WRF transition effort, the Applied Meteorology Unit (AMU) was tasked to configure and implement an operational version of WRF that uses output from ADAS for the model initial conditions. Both agencies asked the AMU to develop a framework that allows the ADAS initial conditions to be incorporated into the WRF Environmental Modeling System (EMS) software. Developed by the NM'S Science Operations Officer (S00) Science and Training Resource Center (STRC), the EMS is a complete, full physics, NWP package that incorporates dynamical cores from both the National Center for Atmospheric Research's Advanced Research WRF (ARW) and the National Centers for Environmental Prediction's Non-Hydrostatic Mesoscale Model (NMM) into a single end-to-end forecasting system. The EMS performs nearly all pre- and postprocessing and can be run automatically to obtain external grid data for WRF boundary conditions, run the model, and convert the data into a format that can be readily viewed within the Advanced Weather Interactive Processing System. The EMS has also incorporated the WRF Standard Initialization (SI) graphical user interface (GUT), which allows the user to set up the domain, dynamical core, resolution, etc., with ease. In addition to the SI GUT, the EMS contains a number of configuration files with extensive documentation to help the user select the appropriate input parameters for model physics schemes, integration timesteps, etc. Therefore, because of its streamlined capability, it is quite advantageous to configure ADAS to provide initial condition data to the EMS software. One of the biggest potential benefits of configuring ADAS for ingest into the EMS is that the analyses could be used to initialize either the ARW or NMM. Currently, the ARPS/ADAS software has a conversion routine only for the ARW dynamical core. However, since the NIvIM runs about 2.5 times faster than the ARW, it is quite advantageous to be able to run an ADAS/NMM configuration operationally due to the increased efficiency.

Case, Jonathan↗
NASA NTRS2012

Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the SPoRT-MODIS GVF dataset on a land surface model (LSM) apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. In the West, higher latent heat fluxes prevailed, which enhanced the rates of evapotranspiration and soil moisture depletion in the LSM. By late Summer and Autumn, both the average sensible and latent heat fluxes increased in the West as a result of the more rapid soil drying and higher coverage of GVF. The impacts of the SPoRT GVF dataset on NWP was also examined for a single severe weather case study using the Weather Research and Forecasting (WRF) model. Two separate coupled LIS/WRF model simulations were made for the 17 July 2010 severe weather event in the Upper Midwest using the NCEP and SPoRT GVFs, with all other model parameters remaining the same. Based on the sensitivity results, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). Portions of the Northern Plains states experienced substantial increases in convective available potential energy as a result of the higher SPoRT/MODIS GVFs. These differences produced subtle yet quantifiable differences in the simulated convective precipitation systems for this event.

Bell, Jordan R.↗