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Paul W Stackhouse, Jr

Publications and source records attributed to Paul W Stackhouse, Jr.

10 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

TPSAS-NF1676L-19256-DND

The NASA/GEWEX (Global Energy and Water Exchanges project) Surface Radiation Budget (SRB) project produces longwave and shortwave radiative fluxes for the surface and top-of-atmosphere (TOA) (see, Stackhouse et al., 2011, GEWEX News). The primary inputs of cloud and meteorology data have been undergoing improvements in quality and spatial and temporal resolution. In anticipation of the release of these data, the SRB longwave flux algorithm has been updated both to accommodate these data and to improve the algorithm used in deriving radiative fluxes. Changes in the inputs and algorithm are as follows: - Synchronized algorithm with version used by NASA CERES (Rose et al. 2006). - Atmospheric aerosols were added. - Beta version of ISCCP H cloud properties used. - Beta version of ISCCP nnHIRS temperature and humidity used. - Option of using a newly derived beta Princeton land surface temperature and diurnal Seaflux sea surface temperature as surface skin temperature added - Updated ozone profile with GISS ozone and MEaSUREs GOZCARDS. - Updated maps of surface topography, vegetation type, and snow/ice by ISCCP. The beta GLW v4 IP has enhanced the output fluxes by adding pristine-sky (no clouds or aerosols) fluxes. The new version also computes fluxes at selected atmospheric levels (including the tropopause) rather than just at the surface and TOA. The purpose of this study is to explore the effect of the above changes on the surface and top-of-atmosphere fluxes. These fluxes are compared to the previous version (release 3.1). We show preliminary results for the mid-seasonal months of 2007.

J Colleen Mikovitz↗
NASA NTRSDate not supplied

TPSAS-NF1676L-31179-DND

This presentation will brief the GEWEX Radiation Panel on the progress and results of the GEWEX SRB project during the last year toward production and assessment of the Release 4.0 data set. First, an update is presented regarding SRB Release 4.0 "Integrated Product" that produces consistent with GEWEX projects estimating land and ocean latent and sensible heat fluxes. Changes from the Release 3/3.1 data products to Release 4 are reviewed both for key inputs (such as the ISCCP HXS, the nn-HIRS, Land and Sea surface temperatures) and key algorithm changes. Differences between the old and new input properties are assessed and flux sensitivities to those inputs are summarized. Sample radiative flux data products are presented and compared against the previous version for span of the production run, currently from 1998 through 2012. These fluxes are also compared to surface measurements (i.e., BSRN) and CERES (Clouds and Earth's Radiant Energy System) data products both in terms of bulk statistics and also time series variability. Lastly, the plans for continued processing, archival, assessment and research and will be presented to the panel for discussion and feedback.

Paul W Stackhouse, Jr↗
NASA NTRSDate not supplied

TPSAS-NF1676L-17940-DND

The NASA/GEWEX SRB (Global Energy and Water Exchanges, Surface Radiation Budget) project produces and archives shortwave and longwave radiation budget flux estimates at the top of the atmosphere and at the Earth's surface. The latest version in the archive, Release 3.0, is available as 3-hourly, 3-hourly-monthly, daily and monthly means continuously over the period from July 1983 to December 2007 on a quasi-equal-area grid system of 44016 grid boxes. SRB Release 4 with further improvements in data quality and higher spatial resolution is being developed. The SRB shortwave/longwave fluxes at the Earth's surface from the algorithm GSW(V3.0)/GLW(V3.1) have been extensively validated against high-quality ground-based observations, in particular observed data from the Baseline Surface Radiation Network (BSRN). Comparisons with nearly 6000 site-months of both shortwave and longwave data from 52 BSRN sites show generally good agreement. In addition, the GEWEX SRB data have also been found to compare favorably with the World Radiation Data Centre (WRDC) data and the Global Energy Balance Archive (GEBA) data. In spite of the fact that the BSRN sites are scattered on all seven continents, the validation of the SRB data over the vast oceans had not been done until recently. In this paper, we present comparisons of the GEWEX-SRB data shortwave/longwave data with observations made on arrays moored in tropical oceans. Specifically, we have data from 21 buoys, or moorings, from Predictions and Research Moored Array in the Atlantic (PIRATA), 14 buoys from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) in the Indian Ocean, 20 buoys from the Tropical Atmosphere Ocean (TAO) array in the Pacific, and 3 buoys from the Woods Hole Oceanographic Institute (WHOI) (2 in the Pacific and 1 in the Atlantic). The data from these buoys span 12 years from 2000 to 2011, though not necessarily continuously. It is found that except for occasional outliers, the majority of the comparable pairs of data points agree well. Further examination of the outliers is necessary to determine whether they can be attributed to wind-driven tilt and/or aerosol accumulation on the instruments. In order to see how the ocean-based validation compares with land-based validation, we made SRB-BSRN monthly mean shortwave/longwave radiation comparisons in the tropical region only, where all the buoys are moored. The results show that in the case of shortwave radiation, the bias error from the ocean-based validation is somewhat larger than its land-based counterpart, but their RMS errors are not significantly different. In the case of longwave radiation, the ocean-based and land-based validations show nearly identical comparison statistics.

Taiping Zhang↗
NASA NTRSDate not supplied

TPSAS-NF1676L-27534-DND

This presentation describes the preparation, validation and provision of MERRA-2 data products to the building industry for use in energy efficient designs, building codes and monitoring energy efficiency. It highlights efforts to use MERRA-2 surface meteorological data products for the derivation of quantities relevant to the energy and building industry such as heating and cooling degree days and building climate zones on a global basis for the past 30 years. A difference of the zones from the first 15 years and last 30 years shows that warming zones are creeping northward. The talk also presents work provide similar parameters for building energy performance assessment requiring data products closer to real time.

Paul W Stackhouse, Jr↗
NASA NTRSDate not supplied

CERES FLASHFlux: Low Latency Data Product for Education and Applied Science Uses

The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed to provide a rapid release version of the CERES results, which are made available to the GLOBE (Global Learning and Observations to Benefit the Environment) program and the applied science research communities involving in the renewable energy and agricultural sectors within a week of the satellite observations by exchanging some accuracy for speed. FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Terra, Aqua, and NOAA-20 observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1-degree equal angle gridded data the combines Terra and Aqua observations. However, since both Terra and Aqua satellites are nearing end of life, the FLASHFlux data product will begin to transition to utilizing NOAA-20 observation in the TISA data product. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the NOAA-20 and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellites have different optics and spectral channels. We quantify the differences in the Level 2 SSF data products between the two satellites over a few sample months for cloud and radiative flux quantities for both the shortwave (solar wavelengths) and longwave (thermal infrared wavelengths) at TOA and surface. In addition, we also assess the impact of using NOAA-20 in place of Aqua in the Level 3 FLASHFlux TISA daily product on the TOA and surface radiative fluxes. Additionally, we highlight use case of FLASHFlux data product in combination with CERES EBAF to assess the Earth’s Radiation Budget, the accuracy of FLASHFlux data product compares to Surface sites measurement, and users of the FLASHFlux data through the POWER (Prediction of Worldwide Energy Resources) program.

PC Sawaengphokhai↗
NASA NTRSDate not supplied

Artificial Neural Network (ANN) Surface Longwave and Shortwave Fluxes Trained on CERES Observations

The Clouds and Earth’s Radiant Energy System (CERES) project provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed to provide key data for the applied sciences and educational users within a week of observation. FLASHFlux achieves this by using simplified calibration, an operational meteorological product from Global Modeling and Assimilation Office (GMAO), and its own surface parameterizations model. The CERES FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Terra and NOAA-20 observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1o x 1o gridded data that combines Terra and NOAA-20 observations. Currently, FLASHFlux uses the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) to derive its surface fluxes (Kratz et al., 2010; Gupta et al, 2001). A new Machine Learning (ML) based approach using Artificial Neural Networks to derive Surface Longwave (LW) & Shortwave (SW) fluxes based on training data from the CERES Clouds Radiative Swath (CRS) product is being investigated to replace LPSA and LPLA in the SSF surface flux products. One of the biggest hurdles in training ML model is model fitting. To overcome the problem of overfitting we use feature engineering that helps in finding the important feature and remove features that are irrelevant to the model. In our training we employed the Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training. We intercompare ANN fluxes against surface fluxes produced from the Fu-Liou model in CRS and the LPSA/LPLA in FLASHFlux SSF. Furthermore, we validated ANN derived fluxes to the Baseline Surface Radiation Network (BSRN).

P C Sawaengphokhai↗