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All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Clouds in GEOS-5

The GEOS-5 atmospheric model is being developed as a weather-and-climate capable model. It must perform well in assimilation mode as well as in weather and climate simulations and forecasts and in coupled chemistry-climate simulations. In developing GEOS-5, attention has focused on the representation of moist processes. The moist physics package uses a single phase prognostic condensate and a prognostic cloud fraction. Two separate cloud types are distinguished by their source: "anvil" cloud originates in detraining convection, and large-scale cloud originates in a PDF-based condensation calculation. Ice and liquid phases for each cloud type are considered. Once created, condensate and fraction from the anvil and statistical cloud types experience the same loss processes: evaporation of condensate and fraction, auto-conversion of liquid or mixed phase condensate, sedimentation of frozen condensate, and accretion of condensate by falling precipitation. The convective parameterization scheme is the Relaxed Arakawa-Schubert, or RAS, scheme. Satellite data are used to evaluate the performance of the moist physics packages and help in their tuning. In addition, analysis of and comparisons to cloud-resolving models such as the Goddard Cumulus Ensemble model are used to help improve the PDFs used in the moist physics. The presentation will show some of our evaluations including precipitation diagnostics.

Bacmeister, Julio↗
NASA NTRS1981

A stochastic-dynamical approach to the study of the natural variability of the climate

A method, suggested by Leith (1975), which employed stochastic-dynamic forecasts obtained from a general circulation model in such a way as to satisfy the definition of climatic noise, was used to validate assumptions accounting for the effects of external influences in estimating the climatic noise. Two assumptions were investigated: (1) that the weather fluctuations can be represented as a Markov process, and (2) that changing external conditions do not influence the atmosphere's statistical properties on short time scales. The general circulation model's simulation of the daily weather fluctuations was generated by performing integrations with prescribed climatological boundary conditions for random initial atmospheric states, with resulting dynamical forecasts providing an ensemble of simulated data for the autoregressive modeling of weather fluctuations. To estimate the climatic noise from the observational data (consisting of hourly values of sea level pressure and surface temperature at 54 U.S. stations for the month of January for the years 1949-1975) use of the short time-scale assumption is made. The simulated and observed data were found not to be consistent with either white noise or a Markov process of weather fluctuations. Good agreement was found between the results of the hypothetical testing of the simulated and the observed surface temperatures; and only partial support was found for the short time-scale assumption, i.e., for sea level pressure.

Straus, D. M.↗
NASA NTRS2020

A Statistical Model to Predict the Extratropical Transition of Tropical Cyclones

This paper introduces a logistic regression model for the extratropical transition(ET) of tropical cyclones in the North Atlantic and the Western North Pacific, using elastic net regularization to select predictors and estimate coefficients.Predictors are chosen from the 1979-2017 best track and reanalysis datasets, and verification is done against the tropical/extratropical labels in the best track data. In an independent test set, the model skillfully predicts ET at lead times up to two days, with latitude and sea surface temperature as its most important predictors. At a lead time of 24 h, it predicts ET with a Matthews correlation coefficient of 0.4 in the North Atlantic, and 0.6 in the Western North Pacific. It identifies 80% of storms undergoing ET in the North Atlantic, and 92% of those in the Western North Pacific. 90% of transition time errors are less than 24 h. Select examples of the model's performance on individual storms illustrate its strengths and weaknesses.Two versions of the model are presented: an "operational model" that may provide baseline guidance for operational forecasts, and a "hazard model"that can be integrated into statistical TC risk models. As instantaneous diagnostics for tropical/extratropical status, both models' zero lead time predictions perform about as well as the widely used Cyclone Phase Space (CPS) in the Western North Pacific and better than the CPS in the North Atlantic, and predict the timings of the transitions better than CPS in both basins.

Melanie Bieli↗
NASA NTRS2004

An Empirical Cumulus Parameterization Scheme for a Global Spectral Model

Realistic vertical heating and drying profiles in a cumulus scheme is important for obtaining accurate weather forecasts. A new empirical cumulus parameterization scheme based on a procedure to improve the vertical distribution of heating and moistening over the tropics is developed. The empirical cumulus parameterization scheme (ECPS) utilizes profiles of Tropical Rainfall Measuring Mission (TRMM) based heating and moistening derived from the European Centre for Medium- Range Weather Forecasts (ECMWF) analysis. A dimension reduction technique through rotated principal component analysis (RPCA) is performed on the vertical profiles of heating (Q1) and drying (Q2) over the convective regions of the tropics, to obtain the dominant modes of variability. Analysis suggests that most of the variance associated with the observed profiles can be explained by retaining the first three modes. The ECPS then applies a statistical approach in which Q1 and Q2 are expressed as a linear combination of the first three dominant principal components which distinctly explain variance in the troposphere as a function of the prevalent large-scale dynamics. The principal component (PC) score which quantifies the contribution of each PC to the corresponding loading profile is estimated through a multiple screening regression method which yields the PC score as a function of the large-scale variables. The profiles of Q1 and Q2 thus obtained are found to match well with the observed profiles. The impact of the ECPS is investigated in a series of short range (1-3 day) prediction experiments using the Florida State University global spectral model (FSUGSM, T126L14). Comparisons between short range ECPS forecasts and those with the modified Kuo scheme show a very marked improvement in the skill in ECPS forecasts. This improvement in the forecast skill with ECPS emphasizes the importance of incorporating realistic vertical distributions of heating and drying in the model cumulus scheme. This also suggests that in the absence of explicit models for convection, the proposed statistical scheme improves the modeling of the vertical distribution of heating and moistening in areas of deep convection.

Rajendran, K.↗
NASA NTRS2021

The Arya Crop Yield Forecasting Algorithm: Application to the Main Wheat Exporting Countries

Wheat is the most important commodity traded in the international food market. Thus, accurate and timely information on wheat production can help mitigate food price fluctuations. Within the existing operational regional and global scale agricultural monitoring systems that provide information on global crop yield and area forecasts, there are still fundamental gaps: #1. Lack of quantitative Earth Observation (EO) derived crop information, #2. Lack of global but detailed (national or subnational level) and timely crop production forecasts and #3. Lack of information on forecast uncertainties. In this study we present the Agriculture Remotely-sensed Yield Algorithm (ARYA) an EO-based method, advancing the state of EO data application and usage (addressing gap #1) to forecast wheat yield. The algorithm is based on the evolution of the Difference Vegetation Index (DVI) using MODIS data at 1km resolution and the Growing Degree Days (GDD) from reanalysis data. Additionally, we explore how Land Surface Temperature (LST) can be included into the model and whether this parameter adds any value to the model performance when combined with the optical information. ARYA is implemented at the national and subnational level to forecast winter wheat yield in the main wheat exporting countries of US, Russia, Ukraine, France, Germany, Australia and Argentina from 2001 to 2019 (covering over 70% of wheat exports globally) in a timely manner by providing daily forecasts (addressing gap #2). The results show that ARYA provides yield estimations with RMSE’s within 0.3 ± 0.1 t/ha at national level and 0.6 ± 0,1 t/ha at subnational level after Day Of the Year (DOY) 140 (mid May) in the Northern Hemisphere and DOY 280 (beginning of October) in the Southern Hemisphere. This means that ARYA can provide crop yield estimates of wheat yield with 5-15 % error at national and 7-20 % error at subnational level starting from 2 to 2.5 months prior to harvest.

Agriculture↗
NASA NTRS1986

The impact of Seasat-A scatterometer data on the numerical prediction of the Queen Elizabeth II storm

The development of the Queen Elizabeth II storm from 1200 UT on September 9, 1978, to 0000 UT on September 11, 1978, is forecast with a limited-area, fine resolution (100 km) numerical weather prediction model. Forecasts from initial conditions that include and exclude Seasat scatterometer surface winds are performed. When the Seasat winds are allowed to influence the upper levels of the atmosphere, a large positive impact is found.

Duffy, D. G.↗
NASA NTRS1991

Automatic analysis of stereoscopic satellite image pairs for determination of cloud-top height and structure

Results are presented on an automatic stereo analysis of cloud-top heights from nearly simultaneous satellite image pairs from the GOES and NOAA satellites, using a massively parallel processor computer. Comparisons of computer-derived height fields and manually analyzed fields show that the automatic analysis technique shows promise for performing routine stereo analysis in a real-time environment, providing a useful forecasting tool by augmenting observational data sets of severe thunderstorms and hurricanes. Simulations using synthetic stereo data show that it is possible to automatically resolve small-scale features such as 4000-m-diam clouds to about 1500 m in the vertical.

Hasler, A. F.↗
NASA NTRS1994

Analyses and forecasts with LAWS winds

Horizontal fluxes of atmospheric water vapor are studied for summer months during 1989 and 1992 over North and South America based on analyses from European Center for Medium Range Weather Forecasts, US National Meteorological Center, and United Kingdom Meteorological Office. The calculations are performed over 20 deg by 20 deg box-shaped midlatitude domains located to the east of the Rocky Mountains in North America, and to the east of the Andes Mountains in South America. The fluxes are determined from operational center gridded analyses of wind and moisture. Differences in the monthly mean moisture flux divergence determined from these analyses are as large as 7 cm/month precipitable water equivalent over South America, and 3 cm/month over North America. Gridded analyses at higher spatial and temporal resolution exhibit better agreement in the moisture budget study. However, significant discrepancies of the moisture flux divergence computed from different gridded analyses still exist. The conclusion is more pessimistic than Rasmusson's estimate based on station data. Further analysis reveals that the most significant sources of error result from model surface elevation fields, gaps in the data archive, and uncertainties in the wind and specific humidity analyses. Uncertainties in the wind analyses are the most important problem. The low-level jets, in particular, are substantially different in the different data archives. Part of the reason for this may be due to the way the different analysis models parameterized physical processes affecting low-level jets. The results support the inference that the noise/signal ratio of the moisture budget may be improved more rapidly by providing better wind observations and analyses than by providing better moisture data.

Wang, Muyin↗
NASA NTRS2002

The JPL Resource Allocation Planning and Scheduling Office (RAPSO) process

The Jet Propulsion Laboratory's Resource Allocation Planning and Scheduling Office is chartered to divide the limited amount of tracking hours of the Deep Space Network amongst the various missions in as equitable allotment as can be achieved. To best deal with this division of assets and time, an interactive process has evolved that promotes discussion with agreement by consensus between all of the customers that use the Deep Space Network (DSN). Aided by a suite of tools, the task of division of asset time is then performed in three stages of granularity. Using this approach, DSN loads are either forecasted or scheduled throughout a moving 10-year window.

RAPSO Resource Allocation Planning and Scheduling ↗
NASA NTRSDate not supplied

Application of Advanced Earth Observations and Model Simulations to Improve Air Quality Monitoring in the Hindu-Kush-Himalayan Region

Air pollution is a serious environmental health concern in the Hindu Kush Himalayan (HKH) region of south-central Asia, as rapid industrialization and population growth have led to increased anthropogenic emissions from transportation, residential, industrial, energy, and biomass burning sources. Natural emissions from dust and forest fires are additional sources of air pollutants that can exacerbate air quality in the region. The combination of the complex pollutant mixtures and atmospherically stable weather conditions during the winter monsoon can visibility reductions and hazardous air quality from persistent haze episodes. The Kathmandu Valley is especially vulnerable to extreme haze issues due to the surrounding mountains that restrict air movement and retains pollutants in the atmosphere. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with ground-based networks to provide a comprehensive data toolkit for advancing air quality monitoring and forecasting decisions in the HKH region. The toolkit includes new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager, which provide high spatiotemporal information on NO2, HCHO, SO2, O3, and aerosol optical depth (AOD). Particulate matter with diameters less than 2.5 micrometers (PM2.5) are derived from the satellite-retrieved AOD using ground-based observations and forecast model data. The satellite observations are also used to initialize and constrain forecast model systems designed for the HKH region. This talk will highlight the performance of the air quality toolkit for enhancing decision-making processes during exceptional air quality events in the region. Note: Presentation includes additional attachment of full presentation with sound and animation (best when viewed as slide show) with runtime of 15 min 32 secs

Aaron Naeger↗
NASA NTRS2001

Improved Use of Satellite Imagery to Forecast Hurricanes

This project tested a novel method that uses satellite imagery to correct phase errors in the initial state for numerical weather prediction, applied to hurricane forecasts. The system was tested on hurricanes Guillermo (1997), Felicia (1997) and Iniki (1992). We compared the performance of the system with and without phase correction to a procedure that uses bogus data in the initial state, similar to current operational procedures. The phase correction keeps the hurricane on track in the analysis and is far superior to a system without phase correction. Compared to operational procedure, phase correction generates somewhat worse 3-day forecast of the hurricane track, but better forecast of intensity. It is believed that the phase correction module would work best in the context of 4-dimensional variational data assimilation. Very little modification to 4DVar would be required.

Louis, Jean-Francois↗
NASA NTRS2003

Performance Expectations of Closed-Brayton-Cycle Heat Exchangers in 100-kWe Nuclear Space Power Systems

Performance expectations of closed-Brayton-cycle heat exchangers to be used in 100-kWe nuclear space power systems were forecast. Proposed cycle state points for a system supporting a mission to three of Jupiter s moons required effectiveness values for the heat-source exchanger, recuperator and rejection exchanger (gas cooler) of 0.98,0.95 and 0.97, respectively. Performance parameters such as number of thermal units (Nm), equivalent thermal conductance (UA), and entropy generation numbers (Ns) varied from 11 to 19,23 to 39 kWK, and 0.019 to 0.023 for some standard heat exchanger configurations. Pressure-loss contributions to entropy generation were significant; the largest frictional contribution was 114% of the heat-transfer irreversibility. Using conventional recuperator designs, the 0.95 effectiveness proved difficult to achieve without exceeding other performance targets; a metallic, plate-fin counterflow solution called for 15% more mass and 33% higher pressure-loss than the target values. Two types of gas-coolers showed promise. Single-pass counterflow and multipass cross-counterflow arrangements both met the 0.97 effectiveness requirement. Potential reliability-related advantages of the cross-countefflow design were noted. Cycle modifications, enhanced heat transfer techniques and incorporation of advanced materials were suggested options to reduce system development risk. Carbon-carbon sheeting or foam proved an attractive option to improve overall performance.

Barrett, Michael J.↗
NASA NTRS2024

Jefferson County Ecological Conservation: Quantifying the Effects of Hydrologic Restoration in the Camas National Wildlife Refuge and Mud Lake Wildlife Management Area

Wetlands in the Camas National Wildlife Refuge and Mud Lake Wildlife Management Area, a rare landscape feature in the Intermountain West, are crucial for migratory birds along the Pacific Flyway, yet these wetlands have experienced a noticeable decline in extent and inundation over the last 40+ years. Partners at the U.S. Fish and Wildlife Service and Idaho Department of Fish and Game have ongoing restoration efforts that are yet to be quantified. To assist these partners, we used NASA Earth observations to quantify change in wetland extent and determine if the restoration projects met the intended impact. The QA_PIXEL band from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) was used to identify water. Landsat 8 OLI imagery was also used for classifying landcover in ArcGIS Pro for 2016 and 2020 and to forecast landcover in 2060 using TerrSet’s Land Change Modeler. We also compared lidar data from 2011 and 2019 to detect changes in vegetation height, ground elevation, and surface water levels, and Sentinel-2 Multispectral Instrument (MSI) imagery to detect changes in vegetation health. Classification results indicated overall accuracies over 84% and Kappa indices above 0.80, well surpassing random classification performance. From 2016 to 2020 our classifications showed an 825 acre increase in wetland extent, and our forecasted 2060 model showed a 2795 acre decrease. Combining our partners’ knowledge of the area with our analyses, we created a remote sensing workflow to enhance future monitoring and decision-making. This study demonstrated the feasibility of NASA Earth observations in quantifying wetlands, informing restoration projects, and supporting avian conservation.

Cassidy Bromka↗
NASA NTRS2000

Accuracy of Modelled Stratospheric Temperatures in the Winter Arctic Vortex from Infra Red Montgolfier Long Duration Balloon Measurements

The temperature of the stratosphere has been measured in the Arctic vortex every 9-10 minutes along the trajectory of four Infra Red Montgolfier long duration balloons flown for 7 to 22 days during the winters of 1997 and 1999. From a number of comparisons to independent sensors, the accuracy of the measurements is demonstrated to be plus or minus 0.5 K during nighttime and at altitude below 28 km (10 hPa). The performances of the analyses of global meteorological models, European Center for Medium Range Weather Forecasts (ECMWF) 31 and 50 levels, United Kingdom Meteorological Office (UKMO), Data Assimilation Office (DAO), National Climatic Prediction Center (NCEP) and NCEP/NCAR reanalysis, used in photochemical simulations of ozone destruction and interpretation of satellite data, are evaluated by comparison to this large (3500 data points) and homogeneous experimental data set. Most of models, except ECMWF31 in 1999, do show a smal1 average warm bias of between 0 and 1.6 K, with deviations particularly large, up to 20 K at high altitude (5hPa) in stratospheric warming conditions in 1999. Particularly wrong was ECMWF 31 levels near its top level at 10 hPa in 1999 where temperature 25 K colder than the real atmosphere were reported. The average dispersion between models and measurements varies from plus or minus 1.0 to plus or minus 3.0 K depending on the model and the year. It is shown to be the result of three contributions. The largest is a long wave modulation likely caused by the displacement of the temperature field in the analyses compared to real atmosphere. The second is the overestimation of the vertical gradient of temperature particularly in warming conditions, which explains the increase of dispersion from 1997 to 1999. Unexpectedly, the third and smallest (plus or minus 0.6-0.7 K) is the contribution of meso and subgrid scale vertical and horizontal features associated to the vertical propagation of orographic or gravity waves. Compared to other models, the newly available ECMWF 50 levels version assimilating the high vertical resolution radiances of the space borne Advanced Microwave Sounding Unit, performs significantly better (0.03 plus or minus 1.12 K on average between 10 and 140 hPa in 1999) than other models.

Pommereau, J.-P.↗
NASA NTRS2003

Performance Expectations of Closed-Brayton-Cycle Heat Exchangers in 100-kWe Nuclear Space Power Systems

Performance expectations of closed-Brayton-cycle heat exchangers to be used in 100-k We nuclear space power systems were forecast. Proposed cycle state points for a system supporting a mission to three of Jupiter's moons required effectiveness values for the heat-source exchanger, recuperator and rejection exchanger (gas cooler) of 0.98, 0.95, and 0.97, respectively. Performance parameters such as number of thermal units (Ntu), equivalent thermal conductance (UA), and entropy generation numbers (Ns) varied from 11 to 19, 23 to 39 kW/K, and 0.019 to 0.023 for some standard heat exchanger configurations. Pressure-loss contributions to entropy generation were significant; the largest frictional contribution was 114% of the heat transfer irreversibility. Using conventional recuperator designs, the 0.95 effectiveness proved difficult to achieve without exceeding other performance targets; a metallic, plate-fin counterflow solution called for 15% more mass and 33% higher pressure-loss than the target values. Two types of gas-coolers showed promise. Single-pass counterflow and multipass cross-counterflow arrangements both met the 0.97 effectiveness requirement. Potential reliability-related advantages of the cross-counterflow design were noted. Cycle modifications, enhanced heat transfer techniques and incorporation of advanced materials were suggested options to reduce system development risk. Carbon-carbon sheeting or foam proved an attractive option to improve overall performance.

Barrett, Michael J.↗
NASA NTRS1986

Performance characteristics of wind profiling radars

Doppler radars used to measure winds in the troposphere and lower stratosphere for weather analysis and forecasting are lower-sensitivity versions of mesosphere-stratosphere-troposphere radars widely used for research. The term wind profiler is used to denote these radars because measurements of vertical profiles of horizontal and vertical wind are their primary function. It is clear that wind profilers will be in widespread use within five years: procurement of a network of 30 wind profilers is underway. The Wave Propagation Laboratory (WPL) has operated a small research network of radar wind profilers in Colorado for about two and one-half years. The transmitted power and antenna aperture for these radars is given. Data archiving procedures have been in place for about one year, and this data base is used to evaluate the performance of the radars. One of the prime concerns of potential wind profilers users is how often and how long wind measurements are lacking at a given height. Since these outages constitute an important part of the performance of the wind profilers, they are calculated at three radar frequencies, 50-, 405-, and 915-MHz, (wavelengths of 6-, 0.74-, and 0.33-m) at monthly intervals to determine both the number of outages at each frequency and annual variations in outages.

Strauch, R. G.↗
NASA NTRS1977

Applications of remote sensing to water resources

Analyses were made of selected long-term (1985 and beyond) objectives, with the intent of determining if significant data-related problems would be encountered and to develop alternative solutions to any potential problems. One long-term objective selected for analysis was Water Availability Forecasting. A brief overview was scheduled in FY-77 of the objective -- primarily a fact-finding study to allow Data Management personnel to gain adequate background information to perform subsequent data system analyses. This report, includes discussions on some of the larger problems currently encountered in water measurement, the potential users of water availability forecasts, projected demands of users, current sensing accuracies, required parameter monitoring, status of forecasting modeling, and some measurement accuracies likely to be achievable by 1980 and 1990.

Source record↗
NASA NTRS2011

Lightning Initiation Forecasting: An Operational Dual-Polarimetric Radar Technique

The objective of this NASA MSFC and NOAA CSTAR funded study is to develop and test operational forecast algorithms for the prediction of lightning initiation utilizing the C-band dual-polarimetric radar, UAHuntsville's Advanced Radar for Meteorological and Operational Research (ARMOR). Although there is a rich research history of radar signatures associated with lightning initiation, few studies have utilized dual-polarimetric radar signatures (e.g., Z(sub dr) columns) and capabilities (e.g., fuzzy-logic particle identification [PID] of precipitation ice) in an operational algorithm for first flash forecasting. The specific goal of this study is to develop and test polarimetric techniques that enhance the performance of current operational radar reflectivity based first flash algorithms. Improving lightning watch and warning performance will positively impact personnel safety in both work and leisure environments. Advanced warnings can provide space shuttle launch managers time to respond appropriately to secure equipment and personnel, while they can also provide appropriate warnings for spectators and players of leisure sporting events to seek safe shelter. Through the analysis of eight case dates, consisting of 35 pulse-type thunderstorms and 20 non-thunderstorm case studies, lightning initiation forecast techniques were developed and tested. The hypothesis is that the additional dual-polarimetric information could potentially reduce false alarms while maintaining high probability of detection and increasing lead-time for the prediction of the first lightning flash relative to reflectivity-only based techniques. To test the hypothesis, various physically-based techniques using polarimetric variables and/or PID categories, which are strongly correlated to initial storm electrification (e.g., large precipitation ice production via drop freezing), were benchmarked against the operational reflectivity-only based approaches to find the best compromise between forecast skill and lead-time. Forecast skill is determined by statistical analysis of probability of detection (POD), false alarm ratio (FAR), Operational Utility Index (OUI), and critical success index (CSI).

Woodard, Crystal J.↗