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NASA NTRS2010

Convective Weather Forecast Accuracy Analysis at Center and Sector Levels

This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60- minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in intensity and location for center forecast are relatively low. For example, 1-hour forecast intensity and horizontal location errors for ZDC center were about 0.12 and 0.13. However, the correlation between sector 1-hour forecast and actual weather coverage was weak, for sector ZDC32, about 32% of the total variation of observation weather intensity was unexplained by forecast; the sector horizontal location error was about 0.10. The paper also introduces an approach to estimate the sector three-dimensional actual weather coverage by using multiple sector forecasts, which turned out to produce better predictions. Using Multiple Linear Regression (MLR) model for this approach, the correlations between actual observation and the multiple sector forecast model prediction improved by several percents at 95% confidence level in comparison with single sector forecast.

Wang, Yao↗
NASA NTRS2014

Observation-Corrected Precipitation Estimates in GEOS-5

Several GEOS-5 applications, including the GEOS-5 seasonal forecasting system and the MERRA-Land data product, rely on global precipitation data that have been corrected with satellite and or gauge-based precipitation observations. This document describes the methodology used to generate the corrected precipitation estimates and their use in GEOS-5 applications. The corrected precipitation estimates are derived by disaggregating publicly available, observationally based, global precipitation products from daily or pentad totals to hourly accumulations using background precipitation estimates from the GEOS-5 atmospheric data assimilation system. Depending on the specific combination of the observational precipitation product and the GEOS-5 background estimates, the observational product may also be downscaled in space. The resulting corrected precipitation data product is at the finer temporal and spatial resolution of the GEOS-5 background and matches the observed precipitation at the coarser scale of the observational product, separately for each day (or pentad) and each grid cell.

Data Assimilation↗
NASA NTRSDate not supplied

Effect of Rain-Adjusted Satellite Sea Surface Salinity on ENSO Predictions from the GMAO S2S Forecast System

ENSO has a significant impact on climate variability throughout the world and so has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of altimetry and in situ data lead to improved short-term forecasts of the coupled system. However, few studies have focused on improving the near-surface density/mixing through assimilation of satellite sea surface salinity (SSS). For expediency, most projects that do assimilate SSS do so as if these data were observed at the top model layer instead of at the surface. In rainy regions where buoyant water sits as a fresh lens at the surface, this assumption is likely invalid. Therefore, we adjust SSS so that it more accurately represents the salinity at the first model layer. The Rain Impact Model (RIM) uses a simple diffusion model to determine the near surface salinity gradient (i.e., 1 cm to 5 m). Satellite SSS data are modified using this near-surface salinity gradient, so the salinity values are now valid at the first model layer (we call this SSS@5m). We assess the impact of satellite SSS observations for near-surface dynamics within ocean reanalyses and how these impact dynamical ENSO forecasts using the NASA GMAO S2S coupled system. For all reanalysis experiments, all available along-track sea level and in situ observations are assimilated for 2011 to 2020 using the LETKF scheme (Penny et al., 2013). One reanalysis assimilates Aquarius /SMAP SSS as before. An additional reanalysis is performed assimilating the SSS@5m data. Validation statistics are compared for experiments that assimilate SSS (sub-optimally as before) versus the SSS@5m. We also compare results of coupled forecasts that are initialized from these reanalyses in spring. For all but the big La Niña in 2017, all NINO3.4 forecasts were improved by using the RIM. We will show that improved SSS estimates upgrades density and near-surface mixing leading to more accurate coupled air/sea interaction and better forecasts.

ENSO↗
NASA NTRS1984

Demand for satellite-provided domestic communications services up to the year 2000

Three fixed service telecommunications demand assessment studies were completed for NASA by The Western Union Telegraph Company and the U.S. Telephone and Telegraph Corporation. They provided forecasts of the total U.S. domestic demand, from 1980 to the year 2000, for voice, data, and video services. That portion that is technically and economically suitable for transmission by satellite systems, both large trunking systems and customer premises services (CPS) systems was also estimated. In order to provide a single set of forecasts a NASA synthesis of the above studies was conducted. The services, associated forecast techniques, and data bases employed by both contractors were examined, those elements of each judged to be the most appropriate were selected, and new forecasts were made. The demand for voice, data, and video services was first forecast in fundamental units of call-seconds, bits/year, and channels, respectively. Transmission technology characteristics and capabilities were then forecast, and the fundamental demand converted to an equivalent transmission capacity. The potential demand for satellite-provided services was found to grow by a factor of 6, from 400 to 2400 equivalent 36 MHz satellite transponders over the 20-year period. About 80 percent of this was found to be more appropriate for trunking systems and 20 percent CPS.

Stevenson, S.↗
NASA NTRSDate not supplied

Improving Ocean Reanalyses and ENSO Forecasts By Assimilation of Rain-Corrected Satellite Sea Surface Salinity Using the GMOA S2s Forecast System

The ENSO phenomenon has a significant global socio-economic impact and has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help improve the initialization of the thermocline, while satellite SST aids in constraining surface heat-fluxes, leading to improved coupled system sub-seasonal to seasonal forecasts. However, few studies have focused on improving the near-surface density and mixing through satellite sea surface salinity (SSS) assimilation. The few ocean models that assimilate satellite SSS, bias correct to normalize towards the near-surface Argo data for expediency. This assumption is likely inadequate in rainy regions, where buoyant water forms a fresh surface lens. In previous work, we showed that adjusting SSS to bulk salinity (Sb) using the Rain Impact Model (RIM) of Santos-Garcia et al., 2014 improves the near-surface density and mixed layer depth, leading to deeper thermocline and improved NINO3.4 SST forecasts. We now utilize the Soil Moisture and Ocean Salinity rain-corrected (SMOS_RC) SSS, available in SMOS-CATDS products, to represent Sb more accurately at the first model layer (e.g., 5 m). Rather than a diffusivity model as RIM, SMOS_RC uses a statistical correction dependent on Integrated Multi-satellitE Retrievals for GPM (IMERG) rain rates, established on observed SMOS SSS decreases related to Sb in the presence of rain (Supply et al., 2020). For all experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF scheme (Penny et al., 2013). One reanalysis additionally assimilates SMOS SSS data as is, and a separate reanalysis assimilates SMOS_RC. We assess the impact on near-surface and subsurface dynamics by validating against observations and explore how SSS assimilation (SMOS vs SMOS_RC) impacts ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (S2S-v3, Molod et al. 2020). We show that improved estimates of density and near-surface mixing led to more accurate coupled air/sea interaction and better ENSO forecasts. The increased SSS, resulting from the removal of the instantaneous rain effect, modifies the ocean state by enhancing mixing and deepening the thermocline.

Veronica Ruiz Xomchuk↗
NASA NTRS2018

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO S2S Forecast System

Sea surface salinity (SSS) observations from space allow us to investigate if improved estimates of near-surface density stratification and associated mixing can positively impact seasonal to interannual variability of tropical Pacific Ocean dynamics as well as dynamical ENSO forecasts. For the first part of the presentation, we utilize our intermediate-complexity coupled model. Baseline experiments assimilate satellite sea level (multi-satellite gridded AVISO, 2013), SST (Reynolds et al., 2004), and in situ subsurface temperature and salinity observations (GTSPP NODC, 2006). These baseline experiments are then compared with experiments that additionally assimilate Aquarius (V5.0 Lilly and Lagerloef, 2008) and SMAP (V4.0 Fore et al., 2016) SSS. Twelve-month forecasts are initialized for each month from September 2011 to September 2017. For initialization of the coupled forecast, the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10S-5N. This pattern enhances air/sea interaction and amplifies the equatorial Kelvin wave signal. We find that including satellite SSS significantly improves NINO3.4 sea surface temperature anomaly validation over most forecast lead times. We next assess how different satellite SSS products impact the validation of ENSO forecasts. SMAP V4 reduces the salty bias in the western Pacific and so is an improvement upon SMAP V2 and SMOS V2 (Boutin et al., 2017) has similar validation characteristics as a combination of Aquarius and SMAP V4. Next we shift to present results from the NASA GMAO Sub-seasonal to seasonal (S2S_v2.1) production coupled model (i.e. the same model that contributes ENSO forecasts to the North American Multi-Model Ensemble Experiment). From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP, allows a unique opportunity to compare and contrast forecasts initialized with the benefit of these two satellite SSS observation types. We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO forecasts for the big 2015 El Nino event. We will present distinct experiments for the overlap period that include 1) freely evolving SSS (i.e. no satellite SSS as the production system), 2) Aquarius, and 3) SMAP initialization. Our results show that using Aquarius slightly improves validation of the reanalysis (including sea level and temperature statistics). Our production system without SSS assimilation generated too warm forecasts for the 2015 El Nino from March initial conditions. Incorporating Aquarius into initialization of the coupled system leads to a deeper, more realistic MLD that acts to damp the downwelling Kelvin signal and slightly cool NINO3.4 SST. With Aquarius the forecasts better match the observed amplitude of the 2015 event. On the other hand, SMAP V2 relaxation generally degrades validation statistics. At forecast initialization, SMAP is much too salty within 10o of the equator, leading to deeper MLD east of 165W. This deeper MLD leads to over-damping of the downwelling signal (i.e. relative upwelling), in turn leading to relatively too cool ENSO forecasts.

Hackert, E.↗
NASA NTRS1974

Some economic benefits of a synchronous earth observatory satellite

An analysis was made of the economic benefits which might be derived from reduced forecasting errors made possible by data obtained from a synchronous satellite system which can collect earth observation and meteorological data continuously and on demand. User costs directly associated with achieving benefits are included. In the analysis, benefits were evaluated which might be obtained as a result of improved thunderstorm forecasting, frost warning, and grain harvest forecasting capabilities. The anticipated system capabilities were used to arrive at realistic estimates of system performance on which to base the benefit analysis. Emphasis was placed on the benefits which result from system forecasting accuracies. Benefits from improved thunderstorm forecasts are indicated for the construction, air transportation, and agricultural industries. The effects of improved frost warning capability on the citrus crop are determined. The benefits from improved grain forecasting capability are evaluated in terms of both U.S. benefits resulting from domestic grain distribution and U.S. benefits from international grain distribution.

Battacharyya, R. K.↗
NASA NTRS1980

Seasonal and daily snowmelt runoff estimates utilizing satellite data

Methods using snowcovered area to update seasonal forecasts as snowmelt progresses are also being used in quasi-operational situations. The input of snowcovered area to snowmelt models for short term perdictions was attempted in two ways; namely, the modification of existing hydrologic models and/or the use of models that were specifically designed to use snowcovered area. A daily snowmelt runoff model was used with LANDSAT data to simulate discharge on remote basins in the Wind River Mountains of Wyoming. Daily predicted and actual flows compare closely, and, summarized over the entire snowmelt season (April 1 - September 30), the average difference is only three percent. The model and snowcovered area data are currently being tested on additional watersheds to determine the method's transferability.

Source record↗
NASA NTRS2020

Dynamical Forecasts of Tropical Terrestrial Carbon Fluxes with the NASA S2S Retrospective Forecast System

Recent advances in the ability to predict climate anomalies at sub-seasonal to seasonal (S2S) timescales allow us to explore the possibility of forecasting carbon flux anomalies. Although carbon flux forecasting is a relatively new concept, it is potentially beneficial as it can help us better understand global and regional land-atmosphere carbon feedbacks associated with climate variations and can provide guidance for future field mission design. Here we evaluate the skill of forecasted terrestrial carbon anomalies generated from meteorological anomalies produced with the NASA Global Modeling and Assimilation Office (GMAO) S2S forecast system. We focus here on three representative time periods (the most recent 2015-2016 El Nino, 2011 La Nina, and 2014 as a neutral year), with each corresponding 9-month forecast comprising four ensemble members initialized in the preceding December. The meteorological variables produced by the GMAO forecast system were bias-corrected using a climatology derived from the Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) before being used to drive a suite of offline simulations with the NASA Catchment-CN terrestrial biosphere model, a model that computes water-energy-carbon dynamics. Forecasts are evaluated by comparing against satellite-driven estimates of gross primary production (GPP) and inverse model estimates of net carbon flux that incorporate satellite carbon dioxide measurements. We find that the restrospectively predicted carbon fluxes in the tropics reasonably reproduce the signs and magnitudes of the observed anomalies between the 2015-2016 El Nino and the 2011 La Nina for both net flux and GPP. For instance, for the El Nino period, the magnitude of the forecasted negative GPP anomaly in the South American tropics (which undergoes anomalously warm and dry conditions) agrees with the observed GPP anomaly at leads of up to three or four months. Overall, this study demonstrates potential skill in the forecast of biospheric carbon fluxes a few months in advance, a capability that could contribute to attribution studies focusing on carbon flux variations and support innovative observation strategies in the future.

Lee, Eunjee↗
NASA NTRS2000

The GEOS Ozone Data Assimilation System: Specification of Error Statistics

A global three-dimensional ozone data assimilation system has been developed at the Data Assimilation Office of the NASA/Goddard Space Flight Center. The Total Ozone Mapping Spectrometer (TOMS) total ozone and the Solar Backscatter Ultraviolet (SBUV) or (SBUV/2) partial ozone profile observations are assimilated. The assimilation, into an off-line ozone transport model, is done using the global Physical-space Statistical Analysis Scheme (PSAS). This system became operational in December 1999. A detailed description of the statistical analysis scheme, and in particular, the forecast and observation error covariance models is given. A new global anisotropic horizontal forecast error correlation model accounts for a varying distribution of observations with latitude. Correlations are largest in the zonal direction in the tropics where data is sparse. Forecast error variance model is proportional to the ozone field. The forecast error covariance parameters were determined by maximum likelihood estimation. The error covariance models are validated using x squared statistics. The analyzed ozone fields in the winter 1992 are validated against independent observations from ozone sondes and HALOE. There is better than 10% agreement between mean Halogen Occultation Experiment (HALOE) and analysis fields between 70 and 0.2 hPa. The global root-mean-square (RMS) difference between TOMS observed and forecast values is less than 4%. The global RMS difference between SBUV observed and analyzed ozone between 50 and 3 hPa is less than 15%.

Stajner, Ivanka↗
NASA NTRS1989

Short-range forecasting and nowcasting, using a simple, isentropic prediction model

A numerical advection model which can be run on a local computer in a real-time forecast environment is described. This isentropic forecast model provides the local forecast office with easy access to temporally and spatially detailed estimates of atmospheric temperature, moisture, and wind field changes between 12-h rawinsonde observations. Case studies are presented based on the use of the model to predict the preconvective environment in spring and summer situations. Short-term forecasts of midtropospheric static stability patterns and stability index changes are traced for several severe storm events with and without the inclusion of surface data observed during the day. Forecast images of VAS low- and midlevel moisture fields and vertical moisture gradients are compared with the observations to determine the utility of the combined model/VAS imagery as a nowcasting guide.

Petersen, Ralph A.↗
NASA NTRS1990

Estimating the cost of major ongoing cost plus hardware development programs

Approaches are developed for forecasting the cost of major hardware development programs while these programs are in the design and development C/D phase. Three approaches are developed: a schedule assessment technique for bottom-line summary cost estimation, a detailed cost estimation approach, and an intermediate cost element analysis procedure. The schedule assessment technique was developed using historical cost/schedule performance data.

Bush, J. C.↗
NASA NTRS1975

Policy issues and data communications for NASA earth observation missions until 1985

The series of LANDSAT sensors with the highest potential data rates of the missions were examined. An examination of LANDSAT imagery uses shows that relatively few require transmission of the full resolution data on a repetitive quasi real time basis. Accuracy of global crop size forecasting can possibly be improved through information derived from LANDSAT imagery. A current forecasting experiment uses the imagery for crop area estimation only, yield being derived from other data sources.

Corte, A. B.↗
NASA NTRS1975

Examination of the costs, benefits and enery conservation aspects of the NASA aircraft fuel conservation technology program

The costs and benefits of the NASA Aircraft Fuel Conservation Technology Program are discussed. Consideration is given to a present worth analysis of the planned program expenditures, an examination of the fuel savings to be obtained by the year 2005 and the worth of this fuel savings relative to the investment required, a comparison of the program funding with that planned by other Federal agencies for energy conservation, an examination of the private industry aeronautical research and technology financial posture for the period FY 76 - FY 85, and an assessment of the potential impacts on air and noise pollution. To aid in this analysis, a computerized fleet mix forecasting model was developed. This model enables the estimation of fuel consumption and present worth of fuel expenditures for selected commerical aircraft fleet mix scenarios.

Source record↗
NASA NTRS1980

Tropical cyclone intensities from satellite microwave data

Radial profiles of mean 1000 mb to 250 mb temperature from the Nimbus 6 scanning microwave spectrometer (SCAMS) were constructed around eight intensifying tropical storms in the western Pacific. Seven storms showed distinct inward temperature gradients required for intensification; the eighth displayed no inward gradient and was decaying 24 hours later. The possibility that satellite data might be used to forecast tropical cyclone turning motion was investigated using estimates obtained from Nimbus 6 SCAMS data tapes of the mean 1000 mb to 250 mb temperature field around eleven tropical storms in 1975. Analysis of these data show that for turning storms, in all but one case, the turn was signaled 24 hours in advance by a significant temperature gradient perpendicular to the storm's path, at a distance of 9 deg to 13 deg in front of the storm. A thresholding technique was applied to the North Central U.S. during the summer to estimate precipitation frequency. except

Vonderhaar, T. H.↗
NASA NTRS1981

The relationship between noise and annoyance around Orly

The extent to which annoyance estimated by an isopsophic index is a good forecaster for annoyance perceived near airport approaches was investigated. An index of sensed annoyance is constructed, and the relationship between the annoyance index and the isopsophic index is studied.

Francois, J.↗
NASA NTRS1981

Notes for Brazil sampling frame evaluation trip

Field notes describing a trip conducted in Brazil are presented. This trip was conducted for the purpose of evaluating a sample frame developed using LANDSAT full frame images by the USDA Economic and Statistics Service for the eventual purpose of cropland production estimation with LANDSAT by the Foreign Commodity Production Forecasting Project of the AgRISTARS program. Six areas were analyzed on the basis of land use, crop land in corn and soybean, field size and soil type. The analysis indicated generally successful use of LANDSAT images for purposes of remote large area land use stratification.

Horvath, R.↗
NASA NTRS1981

AgRISTARS: Foreign commodity production forecasting. The 1980 US corn and soybeans exploratory experiment

The U.S. corn and soybeans exploratory experiment is described which consisted of evaluations of two technology components of a production forecasting system: classification procedures (crop labeling and proportion estimation at the level of a sampling unit) and sampling and aggregation procedures. The results from the labeling evaluations indicate that the corn and soybeans labeling procedure works very well in the U.S. corn belt with full season (after tasseling) LANDSAT data. The procedure should be readily adaptable to corn and soybeans labeling required for subsequent exploratory experiments or pilot tests. The machine classification procedures evaluated in this experiment were not effective in improving the proportion estimates. The corn proportions produced by the machine procedures had a large bias when the bias correction was not performed. This bias was caused by the manner in which the machine procedures handled spectrally impure pixels. The simulation test indicated that the weighted aggregation procedure performed quite well. Although further work can be done to improve both the simulation tests and the aggregation procedure, the results of this test show that the procedure should serve as a useful baseline procedure in future exploratory experiments and pilot tests.

Malin, J. T.↗