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Potter, J.

Publications and source records attributed to Potter, J..

9 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Observations for CMIP5 Simulations

The objective of this is to provide the community of researchers that will access and evaluate the Coupled Model Intercomparison Project Phase 5 (CMIP5) model results access to analogous, analogous sets in terms of periods, variables, temporal/spatial frequency, sets of observational data. A collaborative effort between JPL/NASA and PCMDI is underway to provide the community of researchers that will access and evaluate the CMIP5 model results access to analogous sets of observational data. A number of NASA satellite data sets have been identified that have model equivalents. Thus far: AIRS, MLS, TES, QuikSCAT, CloudSat, Topex/Poseidon, CERES, TRMM, AMSR-E have been identified.

data archive↗
NASA NTRS1979

A simulation study of Large Area Crop Inventory Experiment (LACIE) technology

The author has identified the following significant results. The LACIE performance predictor (LPP) was used to replicate LACIE phase 2 for a 15 year period, using accuracy assessment results for phase 2 error components. Results indicated that the (LPP) simulated the LACIE phase 2 procedures reasonably well. For the 15 year simulation, only 7 of the 15 production estimates were within 10 percent of the true production. The simulations indicated that the acreage estimator, based on CAMS phase 2 procedures, has a negative bias. This bias was too large to support the 90/90 criterion with the CV observed and simulated for the phase 2 production estimator. Results of this simulation study validate the theory that the acreage variance estimator in LACIE was conservative.

Ziegler, L.↗
NASA NTRS1979

Accuracy assessment in the Large Area Crop Inventory Experiment

The Accuracy Assessment System (AAS) of the Large Area Crop Inventory Experiment (LACIE) was responsible for determining the accuracy and reliability of LACIE estimates of wheat production, area, and yield, made at regular intervals throughout the crop season, and for investigating the various LACIE error sources, quantifying these errors, and relating them to their causes. Some results of using the AAS during the three years of LACIE are reviewed. As the program culminated, AAS was able not only to meet the goal of obtaining accurate statistical estimates of sampling and classification accuracy, but also the goal of evaluating component labeling errors. Furthermore, the ground-truth data processing matured from collecting data for one crop (small grains) to collecting, quality-checking, and archiving data for all crops in a LACIE small segment.

Houston, A. G.↗
NASA NTRS1977

Temporal variations in atmospheric water vapor and aerosol optical depth determined by remote sensing

By automatically tracking the sun, a four-channel solar radiometer was used to continuously measure optical depth and atmospheric water vapor. The design of this simple autotracking solar radiometer is presented. A technique for calculating the precipitable water from the ratio of a water band to a nearby nonabsorbing band is discussed. Studies of the temporal variability of precipitable water and atmospheric optical depth at 0.610, 0.8730 and 1.04 microns are presented. There was good correlation between the optical depth measured using the autotracker and visibility determined from National Weather Service Station data. However, much more temporal structure was evident in the autotracker data than in the visibility data. Cirrus clouds caused large changes in optical depth over short time periods. They appear to be the largest deleterious atmospheric effect over agricultural areas that are remote from urban pollution sources.

Pitts, D. E.↗
NASA NTRS1977

Performance tests of signature extension algorithms

Comparative tests were performed on seven signature extension algorithms to evaluate their effectiveness in correcting for changes in atmospheric haze and sun angle in a Landsat scene. Four of the algorithms were cluster matching, and two were maximum likelihood algorithms. The seventh algorithm determined the haze level in both training and recognition segments and used a set of tables calculated from an atmospheric model to determine the affine transformation that corrects the training signatures for changes in sun angle and haze level. Three of the algorithms were tested on a simulated data set, and all of the algorithms were tested on consecutive-day data. The classification performance on the data sets using the algorithms is presented, along with results of statistical tests on the accuracy and proportion estimates. The three algorithms tested on the simulated data produced significant improvements over the results obtained using untransformed signatures. For the consecutive-day data, the tested algorithms produced improvements in most but not all cases. The tests indicated also that no statistically significant differences were noted among the algorithms.

Abotteen, R.↗
NASA NTRS1975

Quantitative determination of stratospheric aerosol characteristics

The author has identified the following significant results. In the S192 data, a peak was apparent in the lower altitudes that was not present in the shorter wavelengths and grew with increasing wavelength beginning with band 7. For ten S192 wavelengths, the relative altitude increment was determined by knowledge of the relative position of the highest point in the scan arc. Using this scheme, results of scaling and inverting data for passes 47 and 61 were put into two models. Each result had three chart representations: (1) limb brightness measurement, (2) attenuation coefficients, and (3) ratio of the aerosol and Rayleigh coefficients to accentuate layers.

Tingey, D. L.↗
NASA NTRS1974

Effect of atmospheric haze and sun angle on automatic classification of ERTS-1 data

The effect of variations in sun angle and haze level on the accuracy of automatic classification of Earth Resources Technology Satellite-1 (ERTS-1) data was studied by classifying ERTS imagery in which such variations were computer-simulated. It was found that relatively small changes in sun angle and haze level can substantially reduce classification accuracy.

Potter, J.↗