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Theoretical relationship between equivalent blackbody temperatures and surface temperatures measured by Nimbus High Resolution Infrared Radiometer /HRIR/
The effects of combined CO2 and CFCl3 and CF2Cl2 time-dependent scenarios on atmospheric O3 and temperature are described; the steady-state levels of O3 and surface temperature, to which the chlorofluoromethane scenario tends in the presence of twice and four time ambient CO2, are examined; and surface temperature changes, caused by the combined effects, are established. A description of the model and of the experiments is presented. Results indicate that (1) the total ozone time history is significantly different from that due to the chlorofluoromethane alone; (2) a local ozone minimum occurs in the upper stratosphere about 45 years from the present with a subsequent ozone increase, then decline; and (3) steady-state solutions indicate that tropospheric temperature and water vapor increases, associated with increased infrared opacity, cause significant changes in tropospheric ozone levels for 2 x CO2 and 4 x CO2, without the addition of chlorofluoromethanes.
The Scanning Multichannel Microwave Radiometer measures thermal microwave emission from the earth in both polarizations at wavelengths of 0.8, 1.4, 1.7, 2.8 and 4.6 cm. Similar instruments were launched on Nimbus 7 and Seasat. Both spatial resolution on the earth and relative sensitivity to different geophysical parameters change with wavelength. Therefore, spatial Fourier components of geophysical parameters are inferred from the corresponding Fourier components of the radiometer measurements, taking into account the different dependence of signal-to-noise ratio on spatial frequency for each radiometer wavelength. The geophysical parameters are sea surface temperature, near-surface wind speed, integrated water vapor mass, integrated liquid water mass, and the product of rainfall rate with height of the rain layer. The capabilities and limitations of the inversion method are illustrated by means of data from the North Atlantic and from tropical storms.
The following monthly mean global distributions for 1993 are presented with a common color scale and geographical map: 10-m height wind speed estimated from the Special Sensor Microwave Imager (SSMI) on a United States (U.S.) Air Force Defense Meteorological Satellite Program (DMSP) spacecraft; sea surface temperature estimated from the Advanced Very High Resolution Radiometer (AVHRR/2) on a U.S. National Oceanic and Atmospheric Administration (NOAA) satellite; 10-m height wind speed and direction estimated from the Active Microwave Instrument (AMI) on the European Space Agency (ESA) European Remote Sensing (ERS-1) satellite; sea surface height estimated from the joint U.S.-France Topography Experiment (TOPEX)/POSEIDON spacecraft; and 10-m height wind speed and direction produced by the European Center for Medium-Range Weather Forecasting (ECMWF). Charts of annual mean, monthly mean, and sampling distributions are displayed.
This report describes a method that can calculate transient aerodynamic heating and transient surface temperatures at supersonic and hypersonic speeds. This method can rapidly calculate temperature and heating rate time-histories for complete flight trajectories. Semi-empirical theories are used to calculate laminar and turbulent heat transfer coefficients and a procedure for estimating boundary-layer transition is included. Results from this method are compared with flight data from the X-15 research vehicle, YF-12 airplane, and the Space Shuttle Orbiter. These comparisons show that the calculated values are in good agreement with the measured flight data.
A heat transfer investigation, which was an extension of a previously reported NACA investigation, was conducted with air flowing through an electrically heated inconel tube with a rounded entrance,an inside diameter of 0.402 inch, and a length of 24 inches over a range of conditions, which included Reynolds numbers up to 500,000, average surface temperatures up to 2050 degrees R, and heat-flux densities up to 150,000 Btu per hour per square foot. Conventional methods of correlating heat-transfer data wherein properties of the air were evaluated at the average bulk, film, and surface temperatures resulted in reductions of Nusselt number of about 38, 46, and 53 percent, respectively, for an increase in surface temperature from 605 degrees to 2050 degrees R at constant Reynolds number. A modified correlation method in which the properties of air were based on the surface temperature and the Reynolds number was modified by substituting the product of the density at the inside tube wall and the bulk velocity for the conventional mass flow per unit cross-sectional area, resulted in a satisfactory correlation of the data for the extended ranges of conditions investigated.
Hydrological models have been calibrated and validated using catchment streamflows. However, using a point measurement does not guarantee correct spatial distribution of model computed heat fluxes, soil moisture and surface temperatures. With the advent of satellites in the late 70s, surface temperature is being measured two to four times a day from various satellite sensors and different platforms. The purpose of this paper is to demonstrate use of satellite surface temperature in (a) validation of model computed surface temperatures and (b) assimilation of satellite surface temperatures into a hydrological model in order to improve the prediction accuracy of soil moistures and heat fluxes. The assimilation is carried out by comparing the satellite and the model produced surface temperatures and setting the "true"temperature midway between the two values. Based on this "true" surface temperature, the physical relationships of water and energy balance are used to reset the other variables. This is a case of nudging the water and energy balance variables so that they are consistent with each other and the true" surface temperature. The potential of this assimilation scheme is demonstrated in the form of various experiments that highlight the various aspects. This study is carried over the Red-Arkansas basin in the southern United States (a 5 deg X 10 deg area) over a time period of a year (August 1987 - July 1988). The land surface hydrological model is run on an hourly time step. The results show that satellite surface temperature assimilation improves the accuracy of the computed surface soil moisture remarkably.
Successful prediction of possible climate change depends on realistic parameterizations of land-surface processes in climate models.
Because the GLAS model's surface fluxes of sensible and latent heat exhibit strong 2 delta t oscillations at the individual grid points as well as in the zonal hemispheric averages and because a basic weakness of the GLAS model lower evaporation over oceans and higher evaporation over land in a typical monthly simulation, the GLAS model PBL parameterization was changed to calculate the mixed layer temperature gradient by solution of a quadratic equation for a stable PBL and by a curve fit relation for an unstable PBL. The new fluxes without any 2 delta t oscillation. Also, the geographical distributions of the surface fluxes are improved. The parameterization presented is incorporated into the new GLAS climate model. Some results which compare the evaporation over land and ocean between old and new calculations are appended.
SEASAT instrumentation payload requirements to provide satellite data for the Navy fleet operational fog prediction program include: (1) some form of C-band microwave radiometer capability; (2) a scanning antenna with a 40-km Instanteneous Field of View (IFOV) for the C-band channel; (3) a narrow band and high resolution IR scanning radiometer for cloud free areas; and (4) a capability for measuring surface winds of 3 to 50 m/sec at + or - 10% accuracy and 50 to 100 km spatial resolution.
The surface temperature of sea ice controls the rate of ice growth and heat exchange between the ocean and the atmosphere. An algorithm for the satellite retrieval of ice surface temperature has recently been published, but due to the lack of validation data has not been extensively tested. In this paper, data from a recent Arctic field experiment is used in an attempt to validate that algorithm. While the procedure is, in principle, straightforward, we demonstrate that validation is complicated by a variety of factors, including incorrectly assumed atmospheric conditions, undetected clouds in the satellite data, spatial and temporal variability in the surface temperature field, and surface and satellite measurement errors. Comparisons between surface temperatures determined from upwelling broadband longwave radiation, spatial measurements of narrow-band radiation, thermocouples buried just below the snow surface, and narrow-band satellite data show differences of 1 to 3 deg. C. The range in these independent measurements indicates the need for specially designed validation experiments utilizing narrow-band radiometers on aircraft to obtain broad spatial coverage.
Coastal upwelling regions are one of the most dynamic areas of the world’s oceans. The California and Baja California Coasts are impacted by both coastal upwelling and the California Current, leading to frontal activity that is captured by gradients in both Sea Surface Temperature (SST) and Sea Surface Salinity (SSS). Satellite data are a great source of spatial data to study fronts. However, biases near coastal areas and coarse resolutions can impair its usefulness in upwelling areas. In this work gradients in SST from NASA Multi-Scale Ultra-High Resolution (MUR) and in two SSS products derived from the Soil Moisture Active Passive (SMAP) NASA mission are compared directly with gradients derived from the Saildrone uncrewed vehicles to validate the gradients as well as to assess their ability to detect known frontal features. The three remotely sensed data sets (MURSST/JPL, SMAP SSS/RSS, SMAP SSS) were co-located with the Saildrone data prior to the calculation of the gradients. Wavelet analysis is used to determine how well the satellite derived SST and SSS products are reproducing the Saildrone derived gradients. Overall results indicate the remote sensing products are reproducing features of known areas of coastal upwelling. Differences between the SST and SSS gradients are mainly associated with the limitations of the microwave derived SSS coverage near land and its reduced spatial resolution. The results are promising for using remote sensing data sets to monitor frontal structure along the California Coast and the application to long term changes in coastal upwelling and dynamics.
The surface temperature TS of the ocean and solid earth can be derived from the radiance data measured in the 3.7 micron transparent region between 2700 and 2500 per cm as well as from the 11 micron water vapor continuum between 960 and 775 per cm. The effects on the accuracy of the recovered values of TS of surface emissivity, reflection of solar radiation and variations in the concentration of water vapor in the atmosphere are different in the two bands. In this paper, the accuracy of the surface temperature derived from each of these two transparent spectral regions is discussed. The possibility of determining the difference between the air temperature at the surface and the true skin surface temperature is also considered.
The primary focus of this project was on the estimation of the three-dimensional, absolute and time-evolving general circulation of the global ocean from a combined analysis of remotely sensed fields of sea surface temperature (SST) and sea surface height (SSH). The synthesis of those two fields was performed with other relevant physical data, and appropriate dynamical ocean models with emphasis on constraining ocean general circulation models by a combination of both SST and SSH data. This effort is directly related to an attempt to describe the mechanisms which give rise to observed SST and its variability on seasonal and inter-annual timescales, its relation to ocean-atmosphere interaction, and the dynamical coupling between the ocean mixed layer and the deep interior ocean. This is one of the fundamental climate related questions being pursued currently under the CLIVAR Program. Because of the strong turbulent mixing associated with atmospheric fluxes of momentum, heat and freshwater through the sea surface, the ocean forms a shallow surface boundary layer, the mixed layer which is largely homogeneous in its constituents. The relation between the temperature of the remotely sensed "skin" and the bulk of the mixed layer is largely understood (Reynolds and Smith 1994; Emery et al., 1995). However, because the surface mixed layer is effectively decoupled from the underlying ocean dynamics, an interpretation of satellite SST observations in isolation and in direct use for dynamical studies is very difficult. As a result, the impact of SST data on the understanding of ocean variability.
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The surface temperature, T(sub s), of a land surface measured by a radiometer, T(sub s,r), and the temperature "felt" by the air, T(sub aero), often differ significantly and are difficult if not impossible to define rigorously. However, recent studies conducted by the principal investigators with several land surface models suggest that this problem can be largely resolved. The main goal of this project was to use model-based and empirical studies to improve understanding and reconcile the difference between T(sub s,r) and T(sub aero), while maintaining consistency within the models and with theory and data. The results from this effort have contributed progress towards the effective use of remotely sensed surface temperature measurements taken from an arbitrary view angle over a partial canopy cover for producing high quality sensible and latent heat flux estimates. In addition, we have developed parameterizations that are designed to improve the representation of the roughness length for heat in climate and mesoscale models. This not only provides improved representation of surface energy balance in such models, but should also facilitate the use of surface temperature measurements for validating or updating the surface temperature produced by SVATs (soil-vegetation-atmosphere schemes) in climate or mesoscale models.
This project was part of a previous grant at MIT that was moved over to the Scripps Institution of Oceanography (SIO) together with the principal investigator. The final report provided here is concerned only with the work performed at SIO since January 2000. The primary focus of this project was the study of the three-dimensional, absolute and time-evolving general circulation of the global ocean from a combined analysis of remotely sensed fields of sea surface temperature (SST) and sea surface height (SSH). The synthesis of those two fields was performed with other relevant physical data, and appropriate dynamical ocean models with emphasis on constraining ocean general circulation models by a combination of both SST and SSH data. The central goal of the project was to improve our understanding and modeling of the relationship between the SST and its variability to internal ocean dynamics, and the overlying atmosphere, and to explore the relative roles of air-sea fluxes and internal ocean dynamics in establishing anomalies in SST on annual and longer time scales. An understanding of those problems will feed into the general discussion on how SST anomalies vary with time and the extend to which they interact with the atmosphere.
Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Communicating diurnal LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.