Brightness Temperature Model Research Articles

Copula-Based Modeling of TMI Brightness Temperature With Rainfall Type

Overland rain retrieval using spaceborne microwave radiometer offers a myriad of complications as land presents itself as a radiometrically warm and highly variable background. Hence, land rainfall algorithms of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in the TMI ocean algorithm). In this paper, sensitivity analysis is conducted using the Spearman rank correlation coefficient as benchmark, to estimate the best combination of TMI low-frequency channels that are highly sensitive to the near surface rainfall rate from the TRMM Precipitation Radar (PR). Results indicate that the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors but also aid in surface noise reduction over a predominantly vegetative land surface background. Furthermore, the variations of rainfall signature in these channel combinations are not understood properly due to their inherent uncertainties and highly nonlinear relationship with rainfall. Copula theory is a powerful tool to characterize the dependence between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this paper proposes a regional model using Archimedean copulas, to study the dependence of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from the passive and active sensors on board TRMM, namely, TMI and PR. Studies conducted for different rainfall regimes over the study area show the suitability of Clayton and Gumbel copulas for modeling convective and stratiform rainfall types for the majority of the intraseasonal months. Furthermore, large ensembles of TMI Tb (from the most sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, and 95th) of the convective and the stratiform rainfall. Comparatively greater ambiguity was observed to model extreme values of the convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal the superior performance of the proposed copula-based technique.

Observation and Modeling of the Microwave Brightness Temperature of Snow-Covered Frozen Lakes and Wetlands

Small-scale variability in land cover influences both the snow cover and the microwave response of a snow-covered surface. Since low microwave frequencies penetrate below the snowpack, the differing dielectric properties of soil and water have a significant effect on passive microwave observations and therefore cause errors in the interpretation of snow parameters from satellite data. Here, the brightness temperature of snow- and ice-covered lakes and wetlands is studied using airborne and spaceborne microwave radiometer observations and modeling of brightness temperature from in situ measurements. We aim at assessing the validity of the multilayer Helsinki University of Technology (HUT) snow emission model on lake- and wetland-rich areas and at examining the error from omission of water bodies in the forward modeling of brightness temperature. The results indicate that the model can estimate brightness temperatures of lakes and wetlands with rms errors of 12-28 K and 9-16 K, respectively. The inclusion of lakes in the satellite-scale simulations reduces the simulation error in 52%-100% of the simulated areas at 18.7 and 36.5 GHz. The inclusion of wetlands further improves simulations, resulting in an rms error of satellite scenes of 4-5 K at 18.7 and 36.5 GHz (5-10 K without lakes and wetlands). However, the natural variability of brightness temperature over water bodies is not entirely captured particularly at 10.65 GHz. The inclusion of lakes and wetlands can be used to reduce errors in the forward model and thus increase the accuracy of snow parameters derived from satellite data.

A MONITORING CAMPAIGN FOR LUHMAN 16AB. I. DETECTION OF RESOLVED NEAR-INFRARED SPECTROSCOPIC VARIABILITY

[abbreviated] We report resolved near-infrared spectroscopic monitoring of the nearby L dwarf/T dwarf binary WISE J104915.57-531906.1AB (Luhman 16AB), as part of a broader campaign to characterize the spectral energy distribution and temporal variability of this system. A continuous 45-minute sequence of low-resolution IRTF/SpeX data spanning 0.8-2.4 micron were obtained, concurrent with combined-light optical photometry with ESO/TRAPPIST. Our spectral observations confirm the flux reversal of this binary, and we detect a wavelength-dependent decline in the relative spectral fluxes of the two components coincident with a decline in the combined-light optical brightness of the system over the course of the observation. These data are successfully modeled as a combination of brightness and color variability in the T0.5 Luhman 16B, consistent cloud variations; and no significant variability in L7.5 Luhman 16A. We estimate a peak-to-peak amplitude of 13.5% at 1.25 micron over the full lightcurve. Using a two-spot brightness temperature model, we infer an average cloud covering fraction of ~30-55% for Luhman 16B, varying by 15-30% over a rotation period. A Rhines scale interpretation for the size of the variable features explains an apparent correlation between period and amplitude for three highly variable T dwarfs, and predicts relatively fast winds (1-3 km/s) for Luhman 16B consistent with lightcurve evolution on an advective time scale (1-3 rotation periods). Our observations support the model of a patchy disruption of the mineral cloud layer as a universal feature of the L dwarf/T dwarf transition.

Evaluation of a Linear Mixing Model to Retrieve Soil and Vegetation Temperatures of Land Targets

A simple linear mixing model of heterogeneous soil-vegetation system and retrieval of component temperatures from directional remote sensing measurements by inverting this model is evaluated in this paper using observations by a thermal camera. The thermal camera was used to obtain multi-angular TIR (Thermal Infra-Red) images over vegetable and orchard canopies. A whole thermal camera image was treated as a pixel of a satellite image to evaluate the model with the two-component system, i.e. soil and vegetation. The evaluation included two parts: evaluation of the linear mixing model and evaluation of the inversion of the model to retrieve component temperatures. For evaluation of the linear mixing model, the RMSE is 0.2 K between the observed and modelled brightness temperatures, which indicates that the linear mixing model works well under most conditions. For evaluation of the model inversion, the RMSE between the model retrieved and the observed vegetation temperatures is 1.6K, correspondingly, the RMSE between the observed and retrieved soil temperatures is 2.0K. According to the evaluation of the sensitivity of retrieved component temperatures on fractional cover, the linear mixing model gives more accurate retrieval accuracies for both soil and vegetation temperatures under intermediate fractional cover conditions.

An Updated Geophysical Model for AMSR-E and SSMIS Brightness Temperature Simulations over Oceans

In this study, we considered the geophysical model for microwave brightness temperature (BT) simulation for the Atmosphere-Ocean System under non-precipitating conditions. The model is presented as a combination of atmospheric absorption and ocean emission models. We validated this model for two satellite instruments—for Advanced Microwave Sounding Radiometer-Earth Observing System (AMSR-E) onboard Aqua satellite and for Special Sensor Microwave Imager/Sounder (SSMIS) onboard F16 satellite of Defense Meteorological Satellite Program (DMSP) series. We compared simulated BT values with satellite BT measurements for different combinations of various water vapor and oxygen absorption models and wind induced ocean emission models. A dataset of clear sky atmospheric and oceanic parameters, collocated in time and space with satellite measurements, was used for the comparison. We found the best model combination, providing the least root mean square error between calculations and measurements. A single combination of models ensured the best results for all considered radiometric channels. We also obtained the adjustments to simulated BT values, as averaged differences between the model simulations and satellite measurements. These adjustments can be used in any research based on modeling data for removing model/calibration inconsistencies. We demonstrated the application of the model by means of the development of the new algorithm for sea surface wind speed retrieval from AMSR-E data.

Radiometric Performance of SMOS Full Polarimetric Imaging

This work has been conducted in the framework of several projects devoted to assess the performance of the Soil Moisture and Ocean Salinity (SMOS) mission full-pol measurement mode. Since its launch in November 2009, SMOS is producing dual-polarization brightness temperature synthesized images that are yielding a high scientific return. However, these images are affected by a non-negligible spatial amplitude error, the so-called spatial bias (SB), that degrades geophysical parameter retrieval. This effect is particularly detrimental in SMOS polarimetric images where spatial bias is masking the polarimetric physical signature to a large extend. This paper presents a method to mitigate SMOS spatial bias by taking into account the co- and cross-polar antenna patterns in the image reconstruction algorithm through the, so called, full-pol G-matrix (FPG). The method is validated by producing spatial bias maps out of the comparison between SMOS full-pol images and an accurate polarimetric brightness temperature model of the ocean. This model has been provided to SMOS ESLs (Expert Support Laboratories) by LOCEAN (Laboratoire d'Oceanographie et du Climat, France) as a test bench to validate and improve SMOS Level 1 (L1) data. Finally, a radiometric performance summary table comparing spatial bias and radiometric sensitivity between this new FPG approach and SMOS current co-polar G-matrix approach (CPG) is provided. This paper presents the best quality SMOS polarimetric images, which may lead a breakthrough in the science returns of the mission.

Analysis of Saturn’s thermal emission at 2.2-cm wavelength: Spatial distribution of ammonia vapor

This work focuses on determining the latitudinal structure of ammonia vapor in Saturn’s cloud layer near 1.5 bars using the brightness temperature maps derived from the Cassini RADAR (Elachi et al. [2004], Space Sci. Rev. 115, 71–110) instrument, which works in a passive mode to measure thermal emission from Saturn at 2.2-cm wavelength. We perform an analysis of five brightness temperature maps that span epochs from 2005 to 2011, which are presented in a companion paper by Janssen et al. (Janssen, M.A., Ingersoll, A.P., Allison, M.D., Gulkis, S., Laraia, A.L., Baines, K., Edgington, S., Anderson, Y., Kelleher, K., Oyafuso, F. [2013]. Icarus, this issue). The brightness temperature maps are representative of the spatial distribution of ammonia vapor, since ammonia gas is the only effective opacity source in Saturn’s atmosphere at 2.2-cm wavelength. Relatively high brightness temperatures indicate relatively low ammonia relative humidity (RH), and vice versa. We compare the observed brightness temperatures to brightness temperatures computed using the Juno atmospheric microwave radiative transfer (JAMRT) program which includes both the means to calculate a tropospheric atmosphere model for Saturn and the means to carry out radiative transfer calculations at microwave frequencies. The reference atmosphere to which we compare has a 3× solar deep mixing ratio of ammonia (we use 1.352×10−4 for the solar mixing ratio of ammonia vapor relative to H2; see Atreya [2010]. In: Galileo’s Medicean Moons – Their Impact on 400 years of Discovery. Cambridge University Press, pp. 130–140 (Chapter 16)) and is fully saturated above its cloud base. The maps are comprised of residual brightness temperatures—observed brightness temperature minus the model brightness temperature of the saturated atmosphere.The most prominent feature throughout all five maps is the high brightness temperature of Saturn’s subtropical latitudes near ±9° (planetographic). These latitudes bracket the equator, which has some of the lowest brightness temperatures observed on the planet. The observed high brightness temperatures indicate that the atmosphere is sub-saturated, locally, with respect to fully saturated ammonia in the cloud region. Saturn’s northern hemisphere storm was also captured in the March 20, 2011 map, and is very bright, reaching brightness temperatures of 166K compared to 148K for the saturated atmosphere model. We find that both the subtropical bands and the 2010–2011 northern storm require very low ammonia RH below the ammonia cloud layer, which is located near 1.5 bars in the reference atmosphere, in order to achieve the high brightness temperatures observed. The disturbances in the southern hemisphere between −42° and −47° also require very low ammonia RH at levels below the ammonia cloud base. Aside from these local and regional anomalies, we find that Saturn’s atmosphere has on average 70±15% ammonia relative humidity in the cloud region. We present three options to explain the high 2.2-cm brightness temperatures. One is that the dryness, i.e., the low RH, is due to higher than average atmospheric temperatures with constant ammonia mixing ratios. The second is that the bright subtropical bands represent dry zones created by a meridionally overturning circulation, much like the Hadley circulation on Earth. The last is that the drying in both the southern hemisphere storms and 2010–2011 northern storm is an intrinsic property of convection in giant planet atmospheres. Some combination of the latter two options is argued as the likely explanation.

Modeling time series of microwave brightness temperature at Dome C, Antarctica, using vertically resolved snow temperature and microstructure measurements

AbstractTime series of observed microwave brightness temperatures at Dome C, East Antarctic plateau, were modeled over 27 months with a multilayer microwave emission model based on dense-medium radiative transfer theory. The modeled time series of brightness temperature at 18.7 and 36.5 GHz were compared with Advanced Microwave Scanning Radiometer–EOS observations. The model uses in situ high-resolution vertical profiles of temperature, snow density and grain size. The snow grain-size profile was derived from near-infrared (NIR) reflectance photography of a snow pit wall in the range 850–1100 nm. To establish the snow grain-size profile, from the NIR reflectance and the specific surface area of snow, two empirical relationships and a theoretical relationship were considered. In all cases, the modeled brightness temperatures were overestimated, and the grain-size profile had to be scaled to increase the scattering by snow grains. Using a scaling factor and a constant snow grain size below 3 m depth (i.e. below the image-derived snow pit grain-size profile), brightness temperatures were explained with a root-mean-square error close to 1 K. Most of this error is due to an overestimation of the predicted brightness temperature in summer at 36.5 GHz.

An Improved C-Band Ocean Surface Emissivity Model at Hurricane-Force Wind Speeds Over a Wide Range of Earth Incidence Angles

An improved microwave radiometric ocean surface emissivity model has been developed to support forward radiative transfer modeling of brightness temperature and geophysical retrieval algorithms for the next-generation airborne Hurricane Imaging Radiometer instrument. This physically based C-band emissivity model extends current model capabilities to hurricane-force wind speeds over a wide range of incidence angles. It was primarily developed using brightness temperature observations during hurricanes with coincident high-quality surface-truth wind speeds, which were obtained using the airborne Stepped-Frequency Microwave Radiometer. Also, other ocean emissivity models available through the published literature and the spaceborne WindSat radiometer measurements were used.

Land Surface Brightness Temperature Modeling Using Solar Insolation

Retrieval of land surface emissivity and temperature from microwave brightness temperature data is a complex problem. The diurnal variation of temperature due to the diurnal cycle of solar radiation and weather conditions makes this problem even more challenging. In this paper, we use solar radiation in modeling the temporal variation of the brightness temperature state of the surface. Solar insolation modeling is used to estimate the diurnal variation of land surface brightness temperature. Solar radiation and brightness temperature are linked through temperature of the surface which is derived based on the radiation balance equation. The temperature state model behaves consistent to the measured temperature data. The root-mean-square (rms) error of the model and measured temperature during 1999 is 1.47 K with a correlation of 0.98. Brightness temperature is calculated as a product of physical temperature and emissivity. This relationship is used to transform the temperature state model into the temporal model of the brightness temperature. The model is validated using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) brightness temperature observations at 10.65-GHz vertical polarization. The rms error of the modeled and measured brightness temperature during 1999 is 2.15 K with a correlation of 0.98. Physical and brightness temperature models are ordinary differential equations that are solved numerically to estimate model parameters. The model parameters are related to geophysical characteristics that modulate the temporal variation of the physical and brightness temperature. These parameters provide new insight into the thermal characteristics of the land surface. Brightness temperature model is used to retrieve emissivity from TMI measurements. Images of emissivity and other model parameters are spatially coherent and reflect ground geometrical and dielectric conditions. The results confirm that incident solar radiation is an important input in modeling the temporal variation in the physical temperature and brightness temperature.

Implementation of the Community Radiative Transfer Model in Advanced Clear‐Sky Processor for Oceans and validation against nighttime AVHRR radiances

The fast Community Radiative Transfer Model (CRTM) has been integrated into National Environmental Satellite Data and Information Service's newly developed Advanced Clear‐Sky Processor for Oceans (ACSPO). CRTM is used in conjunction with the National Centers for Environmental Prediction (NCEP) Global Forecast System atmospheric profiles and Reynolds weekly version 2 sea surface temperatures (SST) to simulate clear‐sky brightness temperatures (BT). Model BTs are used to improve the ACSPO clear‐sky mask, monitor quality of advanced very high resolution radiometer (AVHRR) BTs, and explore physical SST retrievals. This paper documents CRTM implementation in ACSPO version 1 and evaluates nighttime “model minus observation” (M‐O) BT biases in three bands (3.7, 11, and 12 μm) of four AVHRR/3 instruments onboard NOAA‐16, NOAA‐17, NOAA‐18, and MetOp‐A. With careful treatment of input atmospheric and SST data, the agreement is generally good, showing only weak dependencies of M‐O biases on view zenith angle, column water vapor, and wind speed. The agreement improves if Reynolds weekly SST is used instead of NCEP SST. Including surface reflection also reduces the M‐O bias. After all optimizations, the M‐O biases are within several tenths of a Kelvin. Consistency between different platforms is ∼0.1K, except for NOAA‐16 channel 3B, which is biased low compared to other platforms by ∼0.4K. Our future plans include extending the analyses to daytime data and exploring physical SST retrievals. A web‐based tool is being established to continuously monitor the M‐O biases and physical SSTs. The validation methodology employed in this paper will be used to quantitatively measure the effect of each improvement on the M‐O bias and physical SST.

Comparing ERA-40-Based L-Band Brightness Temperatures with Skylab Observations: A Calibration/Validation Study Using the Community Microwave Emission Model

Abstract The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these rms errors. Although the bias between model and observed brightness temperatures are reduced after the calibration, systematic differences in the dynamic range remain. For NWP analysis applications, bias correction schemes should be applied prior to data assimilation. The calibrated model has been used to compute a 10-yr brightness temperature climatology based on ERA-40 data.

Validation of clear-sky radiances over oceans simulated with MODTRAN4.2 and global NCEP GDAS fields against nighttime NOAA15-18 and MetOp-A AVHRR data

An accurate and globally representative forward radiative transfer model (RTM) is needed to explore improvements in sea surface temperature (SST) retrievals from spaceborne infrared observations. This study evaluates the biases in top-of-atmosphere (TOA) brightness temperatures (BT) modeled with the moderate resolution transmission (MODTRAN4.2) band RTM, bounded by a Fresnel's reflective flat sea surface. This model is used to simulate global clear-sky Advanced Very High Resolution Radiometer (AVHRR) nighttime BTs from NOAA-15 through 18 and MetOp-A platforms for one full day of 18 February 2007. Inputs to RTM (SST fields and vertical profiles of atmospheric relative humidity, temperature, pressure, and geopotential height) are specified from the National Centers for Environmental Prediction's (NCEP) Global Data Assimilation System (GDAS) data. Model BTs in AVHRR channels 3B (3.7 μm), 4 (11 μm), and 5 (12 μm) are then compared with their respective measured counterparts, available in the NESDIS operational SST files. Ideally, the RTM should match the observations, but in fact, the modeled BTs are biased high with respect to the AVHRR BTs. The “Model minus Observation” (M − O) bias ranges from about 0 to 2 K, depending upon spectral band, view zenith angle, and sea and atmosphere state at the retrieval point. The bias asymptotically decreases towards confidently clear-sky conditions, but it never vanishes and invariably shows channel-specific dependencies on view zenith angle and geophysical conditions (e.g., column water vapor and sea–air temperature difference). Fuller exploration of the potential of the current RTM (e.g., adding global vertical aerosol profiles) or improvements to its input (NCEP SST and atmospheric profiles) may reduce this bias, but they cannot fully reconcile its spectral and angular structure. The fact that the M − O biases are closely reproducible for five AVHRR sensors flown onboard different platforms adds confidence in the validation approach employed in this study. We emphasize the need for establishing a globally adequate forward RTM for the use in SST modeling and retrievals. A first test of the RTM adequacy is its ability, when used in conjunction with the global fields from the numerical weather prediction models, to reproduce the TOA clear-sky radiances measured by satellite sensors.

Modeling Transmission of Microwaves Through Dynamic Vegetation

In this paper, we develop a model for estimating canopy opacity tau for sweet corn. We estimate the refractive index based upon moisture distribution in the corn during different stages of growth. The moisture distribution was observed during two season-long field experiments. We found that the moisture content decreased linearly as the height of the corn increased, with the distribution closer to Gaussian in the fruit region during reproductive stages. The tau obtained from our model was compared to that estimated using a widely used Jackson model. In general, our tau estimates were higher than those obtained using the Jackson model, with a root mean-square difference (rmsd) of up to 0.23 Np between the two models. The tau values were used in a microwave emission model at C-band, and the model estimates of brightness were compared with field observations. We found that the model brightness temperatures matched well with observations, with rmsd values of 5.13 and 4.88 K, using our model and the Jackson model for tau, respectively.

Effects of the Near-Surface Soil Moisture Profile on the Assimilation of L-band Microwave Brightness Temperature

Abstract The impact of model and observation errors in the European Land Data Assimilation System (ELDAS) data assimilation system on the analyzed surface variables has been studied using the Southern Great Plains Hydrology Experiment (SGP) 1997 and 1999 datasets. The model error for soil moisture was derived from an error propagation experiment based on perturbed rainfall forcing data. It was found that the errors for the top three model layers are 0.010, 0.010, and 0.0015 m3 m−3, respectively. Data assimilation experiments based on screen-level variables (2-m temperature and humidity) and L-band brightness temperature observations from SGP97 with this error distribution result in improved soil moisture forecasts when compared to model runs with a vertically constant model error of 0.005 m3 m−3. In the second part of this study, the effect of the vertical soil moisture distribution—which can hardly be resolved by large-scale hydrological models—in the assimilation system has been quantified using SGP99 data. The vertical profile has a significant impact on the modeled brightness temperatures. Based on the time elapsed between a rainfall event and the observation, a correction scheme has been developed that can be applied in observation space. The assimilation of brightness temperatures led to more accurate predictions of soil moisture and surface fluxes when the correction scheme was used.

Comparison of local scale measured and modelled brightness temperatures and snow parameters from the CLPX 2003 by means of a dense medium radiative transfer theory model

AbstractSome 27 years of passive microwave satellite observations have demonstrated the value of remote sensing for cryospheric applications, albeit at regional to hemispheric scales due to the relatively large sensor footprints (e.g. ∼25 km × 25 km for the advanced microwave scanning radiometer). At the other extreme there is the primarily local‐scale in situ experience of the snow community. Until higher‐resolution sensors become available, understanding the effects of sub‐pixel heterogeneity and the downscaling of observations remains an important topic.In order to provide a baseline for performing scaling studies and for future radiance‐based hydrological assimilation schemes, an evaluation of snowpack forward radiance modelling at the plot scale (least heterogeneous scale) is carried out. Snow microwave radiance models based on dense medium radiative transfer (DMRT) theory incorporate a high degree of physical fidelity, yet dense medium models are particularly sensitive to snowpack structural parameters such as grain size, density, and depth (parameters that may vary substantially within a snowpack). The DMRT performance is evaluated through the use of a fitting technique with respect to snow grain size, a hydrologically important parameter related to the total amount of melt water stored in a snowpack, snowpack metamorphism, vertical energy transport and melt dynamics, as well as various quantities useful in surface energy balance studies. All remaining snow input parameters to the model are derived from snow pit measurements. Model‐predicted radiances are evaluated using microwave brightness measurements collected during the NASA Cold Land Process Experiment (CLPX) by the University of Tokyo's Ground‐Based Microwave Radiometer‐7 (GBMR‐7) system at 18·7, 36·5, and 89 GHz, with incidence angles ranging from 30 to 70 °. The detailed CLPX microwave and snow data provide an excellent opportunity to gain insight into the strengths and weaknesses of this approach. Copyright © 2006 John Wiley & Sons, Ltd.

CORRECTING SOLID PRECIPITATION DATA FROM GAUGE OBSERVATIONS USING PASSIVE MICROWAVE BRIGHTNESS TEMPERATURE DATA

In this paper a basic study is presented to develop a method to correct solid precipitation data from gauge observations using the passive microwave brightness temperature data and data assimilation.Observation and modeling results are indicating a high sensitivity of the 89GHz channel to fresh snow on the ground. The change in the brightness temperature due to snow is used to correct observed solid precipitation data. The old brightness temperature data, the adjusted precipitation and the density of the fresh snow are input parameter for a radiative transfer model. The result is compared with the new brightness temperature and by iteration the precipitation data is adjusted until the modeled value agrees well with the new brightness temperature.Good results have been achieved using snow pit data from Sapporo and modeled brightness temperatures. A sensitivity analysis showed, that this cost minimization problem has a unique solution.

Clear-Sky Window Channel Radiances: A Comparison between Observations and the ECMWF Model

A comparison of clear-sky radiances of the Meteosat window channel with the European Centre for Medium-Range Weather Forecasts (ECMWF) model results is presented, aiming to assess both the model's performance and the quality of the observations. The comparison is made for four periods covering the seasonal cycle and for the Meteosat-7 (Africa and southern Europe) and Meteosat-5 (Middle East and the Indian subcontinent) disks. Results show 1) an underestimation of the diurnal cycle of model brightness temperature in clear-sky conditions with a cool bias during daytime of up to 7–8 K and 2) a much smaller and less widespread warm bias at night. Although such discrepancies are sensitive to surface characteristics (e.g., vegetation type, terrain elevation, and slope), problems in the model surface-to–boundary layer coupling are identified as the most likely model deficiency associated with the underestimation of diurnal amplitudes of skin temperature. A conditional error analysis also reveals strong error stratification with the model cloud cover and with the percent of clear-sky pixels associated with the Meteosat data, suggesting great care should be taken when using Meteosat clear-sky observations, particularly in the ITCZ and adjacent areas.

Subsurface object sensing with a multifrequency microwave radiometer

Experimental results are reported for sensing of subsurface objects with a multifrequency radiometer (MFRAD) system. Properties of the MFRAD system are reviewed, and the calibration and experimental procedures are discussed. Results with subsurface metallic, styrofoam, and plastic targets are then provided that demonstrate an oscillatory behavior in brightness temperatures versus frequency in the presence of a subsurface object. Measured data are also compared with a simple layered medium brightness temperature model and show reasonable agreement with predicted trends of brightness temperatures versus frequency. The oscillatory behaviors versus frequency obtained in the presence of both metallic and nonmetallic subsurface objects should prove advantageous for designing object detection procedures.

ISO LWS Observations of Mars—Detection of Rotational Modulation in the Far Infrared

A series of far infrared (FIR) spectra of Mars between 43 and 196 μm was taken with the Long Wavelength Spectrometer (LWS) on board the European Space Agency's Infrared Space Observatory (ISO). Analysis of these medium-resolution data reveals a rotational modulation of the disk-averaged FIR brightness temperature. The observed variability of ±1.5% in the LWS wavelength range compares favorably with the predictions of a thermophysical model of the martian surface. A surface emissivity of ∼0.90 gives good agreement between the measured and the modeled brightness temperatures and leads to an improved estimate of the LWS photometric calibration uncertainty.