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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Snowmelt Runoff Model in Japan

The preliminary Japanese snowmelt runoff model was modified so that all the input variables arc of the antecedent days and the inflow of the previous day is taken into account. A few LANDSAT images obtained in the past were effectively used to verify and modify the depletion curve induced from the snow water equivalent distribution at maximum stage and the accumulated degree days at one representative point selected in the basin. Together with the depletion curve, the relationship between the basin ide daily snowmelt amount and the air temperature at the point above are exhibited homograph form for the convenience of the model user. The runoff forecasting procedure is summarized.

Ishihara, K.↗
NASA NTRS1999

Simulated Surface Energy Budgets Over the Southeastern US: The GHCC Satellite Assimilation System and the NCEP Early Eta

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite-observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. An advantage of this technique for short-range forecasts (0-48h) is that it does not require a complex land-surface formulation within the atmospheric model. As a result, we can avoid having to specify land surface characteristics such as vegetation resistances, green fraction, leaf area index, soil physical and hydraulic characteristics, stream flow, runoff, and the vertical and horizontal distribution of soil moisture.

Lapenta, William M.↗
NASA NTRS1993

An Algorithm for Forecasting Mountain Wave Related Turbulence in the Stratosphere

A global mountain wave parameterization for prediction of wave related displacements and turbulence is described. The parameterization is used with input from NMC analyses of wind and temperature to examine small-scale disturbances encountered by the NASA high-altitude ER-2 during the Second Airborne Arctic Stratosphere Experiment (AASE-II). The magnitude and location of observed large wave events are well reproduced. A strong correlation is suggested between patches of moderate turbulence encountered by the ER-2 and locations where breaking mountain waves are predicted by the parameterization. These facts suggest that useful forecasts of global mountain wave activity, including wave related CAT, can be made quickly and inexpensively using our mountain wave parameterization with input from current numerical forecast models.

Chan, Roland↗
NASA NTRS1985

Dynamic and static initialization of a mesoscale model using VAS satellite data

Various mesoscale weather forecast model initialization procedures were tested in seven simulation forecasts for a 12 hr period in July 1981. Attention was given to the impacts of using as input radiosonde data, VISSR Atmospheric Sounder (VAS) data and a combination of VAS and data from a smoothed global analysis. The data were assimilated with successive correction objective analysis procedures. The model had a 60 km horizontal grid-length resolution, 14 layers in the vertical, and accounted for short- and long-wave energy in the surface energy budget but not in the atmosphere. Conventional data were used for the mass and moisture fields. The trials showed that VAS data enhanced the predictions of the temperature and mass variables, while winds were least accurately predicted when VAS data were entered in the initializations. Finally, statically initialized precipitation forecasts from radiosonde and/or VAS data exhibited equal skill.

Beauchamp, J. G.↗
DOE OSTI2023

A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses

Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

The Evolution of the Stratopause during the 2006 Major Warming: Satellite Data and Assimilated Meteorological Analyses

Microwave Limb Sounder and Sounding of the Atmosphere with Broadband Emission Radiometry data provide the first opportunity to characterize the four-dimensional stratopause evolution throughout the life-cycle of a major stratospheric sudden warming (SSW). The polar stratopause, usually higher than that at midlatitudes, dropped by 30 km and warmed during development of a major "wave 1" SSW in January 2006, with accompanying mesospheric cooling. When the polar vortex broke down, the stratopause cooled and became ill-defined, with a nearly isothermal stratosphere. After the polar vortex started to recover in the upper stratosphere/lower mesosphere (USLM), a cool stratopause reformed above 75 km, then dropped and warmed; both the mesosphere above and the stratosphere below cooled at this time. The polar stratopause remained separated from that at midlatitudes across the core of the polar night jet. In the early stages of the SSW, the strongly tilted (westward with increasing altitude) polar vortex extended into the mesosphere, and enclosed a secondary temperature maximum extending westward and slightly equatorward from the highest altitude part of the polar stratopause over the cool stratopause near the vortex edge. The temperature evolution in the USLM resulted in strongly enhanced radiative cooling in the mesosphere during the recovery from the SSW, but significantly reduced radiative cooling in the upper stratosphere. Assimilated meteorological analyses from the European Centre for Medium-Range weather Forecasts (ECMWF) and Goddard Earth Observing System Version 5.0.1 (GEOS-5), which are not constrained by data at polar stratopause altitudes and have model tops near 80 km, could not capture the secondary temperature maximum or the high stratopause after the SSW; they also misrepresent polar temperature structure during and after the stratopause breakdown, leading to large biases in their radiative heating rates. ECMWF analyses represent the stratospheric temperature structure more accurately, suggesting a better representation of vertical motion; GEOS-5 analyses more faithfully describe stratopause level wind and wave amplitudes. The high-quality satellite temperature data used here provide the first daily, global, multiannual data sets suitable for assessing and, eventually, improving representation of the USLM in models and assimilation systems.

Manney, Gloria L.↗
NASA NTRS2000

Long-term Global Morphology of Gravity Wave Activity Using UARS Data

An extensive body of research this quarter is documented. Further methodical analysis of temperature residuals in Cryogenic Limb Array Etalon Spectrometer (CLAES) Version 8 level 3AT data show signatures during December 1992 at middle and high northern latitudes that, when compared to Naval Research Laboratory/Mountain Wave Forecast Model (NRL)/(MWFM) mountain wave hindcasts, reveal evidence of long mountain waves in these data over Eurasia, Greenland, Scandinavia and North America. The explicit detection of gravity waves in limb-scanned Cryogenic Infrared Spectrometers and Telescopes for the Atmosphere (CRISTA) temperatures is modeled at length, to derive visibility functions. These insights are used to convert CRISTA gravity wave temperature residuals into data that more closely resemble gravity wave fluctuations detected in data from other satellite instruments, such as Microwave Limb Sounder (MLS), Limb Infrared Monitor of the Stratosphere (LIMS) and Global Positioning System/Meteorology (GPS)/(MET). Finally, newly issued mesospheric temperatures from inversion of CRISTA 15gin emissions are analyzed using a new method that uses separate Kalman fits to the ascending and descending node data. This allows us to study global gravity wave amplitudes at two local times, 12 hours apart. In the equatorial mesosphere, where a large diurnal tidal temperature signal exists, we see modulations of gravity wave activity that are consistent with gravity wave-tidal interactions produced by tidal temperature variability.

Eckermann, Stephen D.↗
NASA NTRS2007

Comparison of Different Global Information Sources Used in Surface Radiative Flux Calculation: Radiative Properties of the Surface

Direct estimates of surface radiative fluxes that resolve regional and weather-scale variabilty over the whole globe with reasonable accuracy have only become possible with the advent of extensive global, mostly satellite, datasets within the past couple of decades. The accuracy of these fluxes, estimated to be about 10-15 W per square meter is largely limited by the accuracy of the input datasets. The leading uncertainties in the surface fluxes are no longer predominantly induced by clouds but are now as much associated with uncertainties in the surface and near-surface atmospheric properties. This study presents a fuller, more quantitative evaluation of the uncertainties for the surface albedo and emissivity and surface skin temperatures by comparing the main available global datasets from the Moderate-Resolution Imaging Spectroradiometer product, the NASA Global Energy and Water Cycle Experiment Surface Radiation Budget project, the European Centre for Medium-Range Weather Forecasts, the National Aeronautics and Space Administration, the National Centers for Environmental Prediction, the International Satellite Cloud Climatology Project (ISCCP), the Laboratoire de Meteorologie Dynamique, NOAA/NASA Pathfinder Advanced Very High Resolution Radiometer project, NOAA Optimum Interpolation Sea Surface Temperature Analysis and the Tropical Rainfall Measuring Mission (TRMM) Microwave Image project. The datasets are, in practice, treated as an ensemble of realizations of the actual climate such that their differences represent an estimate of the uncertainty in their measurements because we do not possess global truth datasets for these quantities. The results are globally representative and may be taken as a generalization of our previous ISCCP-based uncertainty estimates for the input datasets. Surface properties have the primary role in determining the surface upward shortwave (SW) and longwave (LW) flux. From this study, the following conclusions are obtained. Although land surface albedos in the near near-infrared remain poorly constrained (highly uncertain), they do not cause too much error in total surface SW fluxes; the more subtle regional and seasonal variations associated with vegetation and snow are still on doubt. The uncertainty of the broadband black-sky SW albedo for land surface from this study is about 7%, which can easily induce 5-10 W per square meter uncertainty in (upwelling) surface SW flux estimates. Even though available surface (broadband) LW emissivity datasets differ significantly (3%-5% uncertainty), this disagreement is confined to wavelengths greater than 20 micrometers so that there is little practical effect (1-3 W per square meters) on the surface upwelling LW fluxes. The surface skin temperature is one of two leading factors that cause problems with surface LW fluxes. Even though the differences among the various datasets are generally only 2-4 K, this can easily cause 10-15 W per square meter uncertainty in calculated surface (upwelling) LW fluxes. Significant improvements could be obtained for surface LW flux calculations by improving the retrievals of (in order of decreasing importance): (1) surface skin temperature, (2) surface air and near-surface-layer temperature, (3) column precipitable water amount and (4) broadband emissivity. And for surface SW fluxes, improvements could be obtained (excluding improved cloud treatment) by improving the retrievals of (1) aerosols (from our sensitivity studies but not discussed in this work), and (2) surface (black-sky) albedo, of which, NIR part of the spectrum has much larger uncertainty.

Zhang, Yuanchong↗
NASA NTRS2013

Impact of AIRS Thermodynamic Profiles on Precipitation Forecasts for Atmospheric River Cases Affecting the Western United States

This project is a collaborative activity between the NASA Short-term Prediction Research and Transition (SPoRT) Center and the NOAA Hydrometeorology Testbed (HMT) to evaluate a SPoRT Advanced Infrared Sounding Radiometer (AIRS: Aumann et al. 2003) enhanced moisture analysis product. We test the impact of assimilating AIRS temperature and humidity profiles above clouds and in partly cloudy regions, using the three-dimensional variational Gridpoint Statistical Interpolation (GSI) data assimilation (DA) system (Developmental Testbed Center 2012) to produce a new analysis. Forecasts of the Weather Research and Forecasting (WRF) model initialized from the new analysis are compared to control forecasts without the additional AIRS data. We focus on some cases where atmospheric rivers caused heavy precipitation on the US West Coast. We verify the forecasts by comparison with dropsondes and the Cooperative Institute for Research in the Atmosphere (CIRA) Blended Total Precipitable Water product.

Zavodsky, Bradley T.↗
NASA NTRS2000

Long-term Global Morphology of Gravity Wave Activity Using UARS Data

This quarter was largely devoted to a detailed study of temperature data acquired by the Cryogenic Limb Array Etalon Spectrometer (CLAES) on UARS. Our analysis used the same sequence of methods that have been developed, tested and refined on a more limited subset of temperature data acquired by the CRISTA instrument. We focused on a limited subset of our reasoning that geographical and vertical trends in the small-scale temperature variability could be compared with similar trends observed in November 1994 by the CRISTA-SPAS satellite. Results, backed up with hindcasts from the Mountain Wave Forecast Model (MWFM), reveal strong evidence of mountain waves, most persuasively in the Himalayas on 16-17 November, 1992. These CLAES results are coherent over the 30-50 km range and compare well with MWFM hindcasts for the same period. This constitutes, we believe, the first clear evidence that CLAES explicitly resolved long wavelength gravity waves in its CO2 temperature channel. A series of other tasks, related to mesoscale modeling of mountain waves in CRISTA data and fitting of ground-based and HRDI data on global scales, were seen through to publication stage in peer-reviewed journals.

Eckermann, Stephen D.↗
NASA NTRS2018

Impact of Assimilating AIRS Cloud-Cleared Radiances on Atmospheric Dynamics and Polar Low Representation at High Latitudes

This study explores the sensitivity of planetary boundary layer height and related atmospheric dynamics to the assimilation of cloud-cleared AIRS (Atmospheric Infrared Sounder) radiances in the Goddard Earth Observing System (GEOS, version 5) data assimilation and forecast system during the boreal fall 2014 season using observing system experiments (OSEs). Examined here are comparisons between the current, operational approach of assimilating AIRS clear-sky radiances against the assimilation of cloud-cleared radiances (CCR). In polar regions, assimilation of AIRS CCRs is particularly beneficial because of the sparsity of conventional observations and the prevalence of extended low-level stratus cloud cover, which limit the ingestion of clear-sky data. Assimilation of hyperspectral infrared information from AIRS over the Arctic region slightly modifies the lower midtropospheric temperature structure, which in turn contributes to adjustments in geopotential height, affecting the baroclinic instability properties over the entire hemisphere and explaining the overall improvement in global forecast skill. In addition, it is shown that the assimilation of CCRs benefits the representation of convectively-driven small-scale cyclones at high latitudes in the same way as previously noted for tropical cyclones. Specifically, assimilation of CCRs create a temperature dipole over the top of meteorologically active and strongly convective systems such as polar, arctic, and antarctic lows, which helps constrain the analyzed representation of their scale and vertical structure.

Mcgrath-Spangler, E. L.↗
NASA NTRS1985

Preliminary Results from Numerical Experiments on the Summer 1980 Heat Wave and Drought

During the summer of 1980, a prolonged heat wave and drought affected the United States. A preliminary set of experiments has been conducted to study the effect of varying boundary conditions on the GLA model simulation of the heat wave. Five 10-day numerical integrations with three different specifications of boundary conditions were carried out: a control experiment which utilized climatological boundary conditions, an SST experiment which utilized summer 1980 sea-surface temperatures in the North Pacific, but climatological values elsewhere, and a Soil Moisture experiment which utilized the values of Mintz-Serafini for the summer, 1980. The starting dates for the five forecasts were 11 June, 7 July, 21 July, 22 August, and 6 September of 1980. These dates were specifically chosen as days when a heat wave was already established in order to investigate the effect of soil moistures or North Pacific sea-surface temperatures on the model's ability to maintain the heat wave pattern. The experiments were evaluated in terms of the heat wave index for the South Plains, North Plains, Great Plains and the entire U.S. In addition a subjective comparison of map patterns has been performed.

Wolfson, N.↗
DOE OSTI2025

A process-based evaluation of biases in extratropical stratosphere–troposphere coupling in subseasonal forecast systems

Abstract. Two-way coupling between the stratosphere and troposphere is recognized as an important source of subseasonal-to-seasonal (S2S) predictability and can open windows of opportunity for improved forecasts. Model biases can, however, lead to a poor representation of such coupling processes; drifts in a model's circulation related to model biases, resolution, and parameterizations have the potential to feed back on the circulation and affect stratosphere–troposphere coupling. We introduce a set of diagnostics using readily available data that can be used to reveal these biases and then apply these diagnostics to 22 S2S forecast systems. In the Northern Hemisphere, nearly all S2S forecast systems underestimate the strength of the observed upward coupling from the troposphere to the stratosphere, downward coupling within the stratosphere, and the persistence of lower-stratospheric temperature anomalies. While downward coupling from the lower stratosphere to the near surface is well represented in the multi-model ensemble mean, there is substantial intermodel spread likely related to how well each model represents tropospheric stationary waves. In the Southern Hemisphere, the stratospheric vortex is oversensitive to upward-propagating wave flux in the forecast systems. Forecast systems generally overestimate the strength of downward coupling from the lower stratosphere to the troposphere, even as most underestimate the radiative persistence in the lower stratosphere. In both hemispheres, models with higher lids and a better representation of tropospheric quasi-stationary waves generally perform better at simulating these coupling processes.

Garfinkel, Chaim I. (ORCID:000000017258666X)↗
NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 2: Estimation of the urban bias

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data -- the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data -- to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km x 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1 deg x 1 deg latitude-longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative (COOP) Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network (GHCN). Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40 C for monthly minimum temperatures, near 0.25 C for monthly mean temperatures, and near 0.10 C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near -1.1 C for rural stations to 2.4 C for stations from the largest urban areas. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available.

Epperson, David L.↗
NASA NTRS2018

Identifying and Forecasting Potential Biophysical Risk Areas within a Tropical Mangrove Ecosystem Using Multi-Sensor Data

Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities.

Shanti Shrestha↗
DOE OSTI2024

How Do Climate Model Resolution and Atmospheric Moisture Affect the Simulation of Unprecedented Extreme Events Like the 2021 Western North American Heat Wave?

Abstract Although the 2021 Western North America (WNA) heat wave was predicted by weather forecast models, questions remain about whether such strong events can be simulated by global climate models (GCMs) at different model resolutions. Here, we analyze sets of GCM simulations including historical and future periods to check for the occurrence of similar events. High‐ and low‐resolution simulations both encounter challenges in reproducing events as extreme as the observed one, particularly under the present climate. Relatively stronger amplitudes are observed during the future periods. Furthermore, high‐ and low‐resolution short initialized GCM simulations are both able to reasonably predict such strong events and their associated high‐pressure ridge over the WNA with a 1 week forecast lead time. Moisture sensitivity experiments further indicate a drier atmospheric moisture condition results in substantially higher near‐surface temperatures in the simulated heat events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An Urban Climate Archipelago in the Southeastern U.S.

Urban boundary layers (UBLs; Barlow, 2014) are the most populous microclimate in the world but are still a challenge to represent physically due to the lack of frequent observations within most urban centers. We examine a set of inland urban regions of different sizes within the Southeast U.S.: Atlanta, GA; Athens, GA; Augusta, GA; and Columbia, SC. We compare Weather Research and Forecasting (WRF; Skamarock et al., 2019) simulation output of Atlanta to hourly-averaged Aircraft Meteorological Data Relay (AMDAR) temperature profiles within the warm season in the ATL airport. The scaling of the simulated UBL by size of the urban region is assessed by comparing the UBL dynamics of the three smaller cities (Athens, Augusta, and Columbia) to Atlanta.

Wermter, Jospeh↗