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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.

Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.

Storm↗
NASA NTRS2017

Data Assimilation to Extract Soil Moisture Information From SMAP Observations

Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, they can be used to reduce the need for localized bias correction techniques typically implemented in data assimilation (DA) systems that tend to remove some of the independent information provided by satellite observations. Here, we use a statistical neural network (NN) algorithm to retrieve SMAP (Soil Moisture Active Passive) surface soil moisture estimates in the climatology of the NASA Catchment land surface model. Assimilating these estimates without additional bias correction is found to significantly reduce the model error and increase the temporal correlation against SMAP CalVal in situ observations over the contiguous United States. A comparison with assimilation experiments using traditional bias correction techniques shows that the NN approach better retains the independent information provided by the SMAP observations and thus leads to larger model skill improvements during the assimilation. A comparison with the SMAP Level 4 product shows that the NN approach is able to provide comparable skill improvements and thus represents a viable assimilation approach.

D↗
NASA NTRS1990

Determination of the ocean circulation using Geosat altimetry

A spherical harmonic model of the sea surface topography complete to degree and order 10 and a model of the earth's geopotential field complete to degree and order 50 have been obtained in a simultaneous solution using Geosat altimeter data and tracking data from 14 different satellites. The sea surface topography model compares well with oceanographic models computed using hydrographic data and ship drift data. Currently, errors in the estimated gravity field model limit the determination of the spherical harmonic coefficients of the general ocean circulation to degrees 10 and lower, corresponding to a minimum wavelength of 4000 km. Error analysis indicates that the correlation between the geoid and the sea surface topography model is less than 0.2, indicating good separation of the geoid and the sea surface topography at wavelengths of 4000 km or longer. Estimates of the scale factor for the significant wave height (H1/3), which is used to compute the electromagnetic bias correction and the bias for the Geosat altimeter, are obtained. The estimate of the H1/3 correction is 3.6 + or - 1.5 percent, and the height bias estimate is zero.

Nerem, R. S.↗
NASA NTRS2017

Effective Assimilation of SMAP Observations Using Statistical Techniques

Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, the need for bias correction prior to an assimilation of these estimates is reduced, which could result in a more effective use of the independent information provided by the satellite observations. In this study, a statistical neural network (NN) retrieval algorithm is calibrated using SMAP brightness temperature observations and modeled soil moisture estimates (similar to those used to calibrate the SMAP Level 4 DA system). Daily values of surface soil moisture are estimated using the NN and then assimilated into the NASA Catchment model. The skill of the assimilation estimates is assessed based on a comprehensive comparison to in situ measurements from the SMAP core and sparse network sites as well as the International Soil Moisture Network. The NN retrieval assimilation is found to significantly improve the model skill, particularly in areas where the model does not represent processes related to agricultural practices. Additionally, the NN method is compared to assimilation experiments using traditional bias correction techniques. The NN retrieval assimilation is found to more effectively use the independent information provided by SMAP resulting in larger model skill improvements than assimilation experiments using traditional bias correction techniques.

Kolassa, J.↗
NASA NTRSDate not supplied

Improving the Use of Surface-Sensitive Radiances in the GMAO Hybrid-4DEnVar System

The planetary boundary layer (PBL) was designated as an incubation-class targeted observable (TO) in the 2018 Decadal Survey. As no single instrument source will provide enough information to constrain the global PBL, the assimilation of a wide range of observations in data assimilation (DA) systems will play a critical role, and GMAO has put in efforts to enhance surface-sensitive radiance assimilation. Although a vast number of microwave radiance observations are used in the GEOS global DA system, very few surface-sensitive radiances are currently used over land due to large uncertainty of land surface emissivity in the CRTM as well as cloud detection issue. Dynamically varying emissivity is retrieved from observations of window channels in the GEOS system for non-scattering FOVs and applied to sounding channel assimilation. Moreover, the original radiance bias correction is found not to work well over land, and shows drifting bias when the original emissivity sensitivity bias predictor with a dynamically varying emissivity retrieval is used. Hence, the radiance bias correction has been modified, and the quality control procedure has been adapted accordingly. Cycled experiment results show improvement in the temperature forecast at lower model levels; further refinement of this system continues.

Yanqiu Zhu↗
NASA NTRS2022

Present-Day and Future PM 2.5 and O 3 -Related Global and Regional Premature Mortality in the EVAv6.0 Health Impact Assessment Model

We used the EVAv6.0 system to estimate the present (2015) and future (2015–2050) global PM 2.5 and O 3 -related premature mortalities, using simulated surface concentrations from the GISS-E2.1-G Earth system model. The PM 2.5 -related global premature mortality is estimated to be 4.3 and 4.4 million by the non-linear and linear models, respectively. Ischemic heart diseases are found to be the leading cause of PM 2.5 -related premature deaths, contributing by 35% globally. Both long-term and short-term O 3 -related premature deaths are estimated to be around 1 million, globally. Overall, PM 2.5 and O 3 -related premature mortality leads to 5.3–5.4 million premature deaths, globally. The global burden of premature deaths is mainly driven by the Asian region, which in 2015 contributes by 75% of the total global premature deaths. An increase from 6.2% to 8% in the PM 2.5 relative risk as recommended by the WHO leads to an increase of PM 2.5 -related premature mortality by 28%, to 5.7 million. Finally, bias correcting the simulated PM 2.5 concentrations in 2015 leads to an increase of up to 73% in the global PM 2.5 -related premature mortality, leading to a total number of global premature deaths of up to 7.7 million, implying the necessity of bias correction to get more robust health burden estimates. PM 2.5 and O 3 -related premature mortality in 2050 decreases by up to 57% and 18%, respectively, due to emission reductions alone. However, the projected increase and aging of the population leads to increases of premature mortality by up to a factor of 2, showing that the population exposed to air pollution is more important than the level of air pollutants, highlighting that the population dynamics should be considered when setting up health assessment systems.

Premature mortality↗
NASA NTRS1996

Distortion Representation of Forecast Errors for Model Skill Assessment and Objective Analysis

We study a novel characterization of errors for numerical weather predictions. In its simplest form we decompose the error into a part attributable to phase errors and a remainder. The phase error is represented in the same fashion as a velocity field and will be required to vary slowly and smoothly with position. A general distortion representation allows for the displacement and a bias correction of forecast anomalies. In brief, the distortion is determined by minimizing the objective function by varying the displacement and bias correction fields. In the present project we use a global or hemispheric domain, and spherical harmonics to represent these fields. In this project we are initially focusing on the assessment application, restricted to a realistic but univariate 2-dimensional situation. Specifically we study the forecast errors of the 500 hPa geopotential height field for forecasts of the short and medium range. The forecasts are those of the Goddard Earth Observing System data assimilation system. Results presented show that the methodology works, that a large part of the total error may be explained by a distortion limited to triangular truncation at wavenumber 10, and that the remaining residual error contains mostly small spatial scales.

Hoffman, Ross N.↗
NASA NTRS2015

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

simulation↗
NASA NTRS2017

The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is NASA’s latest reanalysis for the satellite era (1980-present) using the Goddard Earth Observing System version 5 (GEOS-5) Earth system model. MERRA-2 provides several improvements over its predecessor (MERRA), including the inclusion of interactive aerosols for the entire period. In addition to assimilating bias-corrected Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, it also includes the assimilation of bias-corrected AOD from Advanced Very High Resolution Spectroradiometer (AVHRR) instruments, Multi-angle Imaging SpectroRadiometer (MISR) AOD over bright surfaces, and ground-based Aerosol Robotic Network (AERONET) AOD. This paper is the second of a pair that summarizes our efforts to assess the quality of the MERRA-2 aerosol assimilation. In this study, we first follow previous work performed with version 1 of the MERRA Aerosol Reanalysis (MERRAero) using independent observations. The evaluation of MERRA-2 Absorption Aerosol Optical Depth (AAOD) and ultra-violet Aerosol Index (UV-AI) against the Ozone Monitoring Instrument (OMI) observations show good agreement, particularly over dusty regions where our previous efforts improved model aerosol optical properties. Next, we find that aerosol assimilation system improves the aerosol vertical structure when compared to estimates from the same version of the model without AOD assimilation. A similar conclusion is found for MERRA-2 aerosol surface fine particulate matter (PM (sub 2.5)). However, deficiencies in the forward model such as missing emissions noted during the MERRAero study still explain the MERRA-2 PM(sub 2.5) bias relative to observations over the United States. Finally, to illustrate successes and weaknesses of the AOD assimilation, we focus on the performance of the MERRA-2 aerosol system during several major aerosol events: the Mount Pinatubo eruption in 1991, a Saharan dust event and transportation over the Atlantic Ocean during April 2010, the Rim Fire of summer 2013 in California, and an extreme pollution event over China in January 2013. We conclude with a summary that points to best practices for utilizing the MERRA-2 aerosol reanalysis in future studies.

V Buchard↗
NASA NTRS1994

The sea state bias in altimeter estimates of sea level from collinear analysis of TOPEX data

The wind speed and significant wave height (H(sub 1/3)) dependencies of the sea state bias in altimeter estimates of sea level, expressed in the form (Delta)h(sub SSB) = bH(sub 1/3), are examined from least squares analysis of 21 cycles of collinear TOPEX data. The bias coefficient b is found to increase in magnitude with increasing wind speed up to about 12 m/s and decrease monotonically in magnitude with increasing H(sub 1/3). A parameterization of b as a quadratic function of wind speed only, as in the formation used to produce the TOPEX geophysical data records (GDRs), is significantly better than a parameterization purely in terms of H(sub 1/3). However, a four-parameter combined wind speed and wave height formulation for b (quadratic in wind speed plus linear in H(sub 1/3)) significantly improves the accuracy of the sea state bias correction. The GDR formulation in terms of wind speed only should therefore be expanded to account for a wave height dependence of b. An attempt to quantify the accuracy of the sea state bias correction (Delta)h(sub SSB) concludes that the uncertainty is a disconcertingly large 1% of H(sub 1/3).

Chelton, Dudley B.↗
NASA NTRS2019

Long-Term Ozone Variability and Trends from Reanalyses: Can It Be Done?

Stratospheric ozone concentrations have begun to show early signs of recovery following the implementation of the Montreal Protocol and its amendments as well as in response to decreasing upper-stratospheric temperatures. Secular trends in stratospheric ozone are modulated by considerable interannual variability and systematic changes in transport patterns that are expected under increasing concentrations of greenhouse gases, especially in the lower stratosphere. These factors necessitate the continued close monitoring of stratospheric ozone in upcoming decades, with a special focus on the lower stratosphere.As highly resolved data sets combining a plethora of observations with model simulations atmospheric reanalyses are, in principle, well suited for the task. All major reanalyses generate ozone output. However, significant spurious discontinuities that arise from step changes in the observing systems prevent a straightforward analysis of ozone trends and long-term variability. Building on our recent work, in this presentation we will demonstrate that trend detection is nonetheless possible using the ozone record from NASA's MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2) reanalysis bias-corrected using a chemistry model simulation as a transfer function. Next, we will outline several strategies to reduce artificial discontinuities in the ozone record in future NASA reanalyses. This discussion will be illustrated by an example of joint assimilation of bias-corrected ozone profiles from the Microwave Limb Sounder (MLS) on the Aura satellite (2004 to present) and the Ozone Mapping Profiler Suite Limb Profiler (OMPS-LP) sensors that are expected to operate on future NOAA platforms.

Wargan, Krzysztof↗
NASA NTRS2003

Unexplained Discontinuity in the US Radiosonde Temperature Data: Stratosphere - Part 2

In part I of this paper, the United States (US) radiosonde temperature data are shown to have significant and unexplained inhomogeneities in the mid-troposphere. This part discusses the differences between observations taken at 0 and 12 UTC especially in the stratosphere by the Vaisala RS80 radiosondes that are integrated within the National Weather Service's (NWS) Micro-ART system. The results show that there is a large maxima in the horizontal distribution of the monthly means of the 0/12 UTC differences over the central US that is absent over Canada and this maxima is as large as 5 C at 10 hPa. The vertical profiles of the root-mean-square of the monthly means are much larger in the US than those else where. The data clearly shows that the 0/12 UTC differences are largely artificial especially over the central US and originate in the post processing software at observing stations, thus confirming the findings in part I. Special flight data from the NWS's test facility at Sterling, Va. have been obtained. This data can be used to deduce the bias correction applied by Vaisala's post processing system. By analyzing the correction data, it can be shown that the inconsistencies with non-US Vaisala RS80 data as well as most of the large 0/12 UTC differences over the US can be accounted for by multiplying the reported elapsed time (i.e. time since launch) by the factor which is incorrectly applied by the post processing software. After being presented with the findings in this paper, Vaisala further isolated the source of the inconsistencies to a software coding error in the radiation bias correction scheme. The error effects only the software installed at US stations.

Redder, Christopher R.↗
NASA NTRS2008

Accuracy Assessment and Correction of Vaisala RS92 Radiosonde Water Vapor Measurements

Relative humidity (RH) measurements from Vaisala RS92 radiosondes are widely used in both research and operational applications, although the measurement accuracy is not well characterized as a function of its known dependences on height, RH, and time of day (or solar altitude angle). This study characterizes RS92 mean bias error as a function of its dependences by comparing simultaneous measurements from RS92 radiosondes and from three reference instruments of known accuracy. The cryogenic frostpoint hygrometer (CFH) gives the RS92 accuracy above the 700 mb level; the ARM microwave radiometer gives the RS92 accuracy in the lower troposphere; and the ARM SurTHref system gives the RS92 accuracy at the surface using 6 RH probes with NIST-traceable calibrations. These RS92 assessments are combined using the principle of Consensus Referencing to yield a detailed estimate of RS92 accuracy from the surface to the lowermost stratosphere. An empirical bias correction is derived to remove the mean bias error, yielding corrected RS92 measurements whose mean accuracy is estimated to be +/-3% of the measured RH value for nighttime soundings and +/-4% for daytime soundings, plus an RH offset uncertainty of +/-0.5%RH that is significant for dry conditions. The accuracy of individual RS92 soundings is further characterized by the 1-sigma "production variability," estimated to be +/-1.5% of the measured RH value. The daytime bias correction should not be applied to cloudy daytime soundings, because clouds affect the solar radiation error in a complicated and uncharacterized way.

Whiteman, David N.↗
NASA NTRSDate not supplied

The Use of the BSRN Data as A Benchmark for the POWER Hourly DHI and DNI and In Validating Derived Hourly GTI

The satellite-based CERES SYN1deg hourly data is the source data of the POWER GIS solar data that covers 2001 to near present. The SYN1deg(Ed4.1) hourly GHI agrees well with the BSRN data, but the hourly DHI and DirHI (Direct Horizontal Irradiance) are positively and negatively, respectively, biased with appreciable magnitudes. The hourly DNI, derived by dividing the DirHI by cos(SZA), or the cosine of the solar zenith angle, is therefore negatively biased. Based on the statistics of comparisons with the BSRN data, we performed bias corrections on the hourly DHI and DNI. The corrections were executed in the 3-D phase space of latitude, cos(SZA), and cloud fraction (CLFR). The isotropic model is then used to derive the hourly global tilted irradiance (GTI). For validation purpose, we applied the isotropic model to the BSRN data at the original 1-, 2-, 3- or 5-minute interval. The satellite-based hourly GTI shows good agreement with their BSRN counterpart. We also examined two monthly-mean-based methods that empirically derive monthly mean GTI and DNI from monthly mean GHI and from both monthly mean GHI and DHI. The monthly-mean-based results compare favorably with the hourly-mean-based results. The GEWEX SRB (V4-IP) provides POWER with daily mean GHI for the years before the CERES era, and the data were corrected using quantile mapping by referencing the CERES SYN1deg data. We used the Kolmogorov -Smirnov test (K-S test) and Cramer-von Mises test to examine how well the results agree with the BSRN data. We found that if we set the lower limit for the daily mean GHI to 30 W m-2, the data can pass the K-S test at 0.01 significance level and the Cramer-von Mises test at 0.001 significance level. If no lower limit is set on the daily means, the data fail both tests. The satellite-based CERES SYN1deg hourly data is the source data of the POWER GIS solar data that covers 2001 to near present. The SYN1deg(Ed4.1) hourly GHI agrees well with the BSRN data, but the hourly DHI and DirHI (Direct Horizontal Irradiance) are positively and negatively, respectively, biased with appreciable magnitudes. The hourly DNI, derived by dividing the DirHI by cos(SZA), or the cosine of the solar zenith angle, is therefore negatively biased. Based on the statistics of comparisons with the BSRN data, we performed bias corrections on the hourly DHI and DNI. The corrections were executed in the 3-D phase space of latitude, cos(SZA), and cloud fraction (CLFR). The isotropic model is then used to derive the hourly global tilted irradiance (GTI). For validation purpose, we applied the isotropic model to the BSRN data at the original 1-, 2-, 3- or 5-minute interval. The satellite-based hourly GTI shows good agreement with their BSRN counterpart. We also examined two monthly-mean-based methods that empirically derive monthly mean GTI and DNI from monthly mean GHI and from both monthly mean GHI and DHI. The monthly-mean-based results compare favorably with the hourly-mean-based results. The GEWEX SRB (V4-IP) provides POWER with daily mean GHI for the years before the CERES era, and the data were corrected using quantile mapping by referencing the CERES SYN1deg data. We used the Kolmogorov -Smirnov test (K-S test) and Cramer-von Mises test to examine how well the results agree with the BSRN data. We found that if we set the lower limit for the daily mean GHI to 30 W m-2, the data can pass the K-S test at 0.01 significance level and the Cramer-von Mises test at 0.001 significance level. If no lower limit is set on the daily means, the data fail both tests.

Taiping Zhang↗
NASA NTRS2018

Surface Irradiances of Edition 4.0 Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Data Product

The algorithm to produce the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4.0 (Ed4) Energy Balanced and Filled (EBAF)-surface data product is explained. The algorithm forces computed topof- atmosphere (TOA) irradiances to match with Ed4 EBAF-TOA irradiances by adjusting surface, cloud, and atmospheric properties. Surface irradiances are subsequently adjusted using radiative kernels. The adjustment process is composed of two parts: bias correction and Lagrange multiplier. The bias in temperature and specific humidity between 200 and 500 hPa used for the irradiance computation is corrected based on observations by Atmospheric Infrared Sounder (AIRS). Similarly, the bias in the cloud fraction is corrected based on observations by Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat. Remaining errors in surface, cloud, and atmospheric properties are corrected in the Lagrange multiplier process. Ed4 global annual mean (January 2005 through December 2014) surface net shortwave (SW) and longwave (LW) irradiances increase by 1.3Wm22 and decrease by 0.2Wm22, respectively, compared to EBAF Edition 2.8 (Ed2.8) counterparts (the previous version), resulting in an increase in net SW 1LWsurface irradiance of 1.1Wm22. The uncertainty in surface irradiances over ocean, land, and polar regions at various spatial scales are estimated. The uncertainties in all-sky global annual mean upward and downward shortwave irradiance are 3 and 4Wm22, respectively, and the uncertainties in upward and downward longwave irradiance are 3 and 6Wm22, respectively. With an assumption of all errors being independent, the uncertainty in the global annual mean surface LW 1 SW net irradiance is 8Wm22.

Kato, Seiji↗
NASA NTRS1974

GEOS-C altimeter attitude bias error correction

A pulse-limited split-gate-tracking radar altimeter was flown on Skylab and will be used aboard GEOS-C. If such an altimeter were to employ a hypothetical isotropic antenna, the altimeter output would be independent of spacecraft orientation. To reduce power requirements the gain of the altimeter antenna proposed is increased to the point where its beamwidth is only a few degrees. The gain of the antenna consequently varies somewhat over the pulse-limited illuminated region of the ocean below the altimeter, and the altimeter output varies with antenna orientation. The error introduced into the altimeter data is modeled empirically, but close agreements with the expected errors was not realized. The attitude error effects expected with the GEOS-C altimeter are modelled using a form suggested by an analytical derivation. The treatment is restricted to the case of a relatively smooth sea, where the height of the ocean waves are small relative to the spatial length (pulse duration times speed of light) of the transmitted pulse.

Marini, J. W.↗