Satellite-observed Brightness Temperatures Research Articles

Convectively Coupled Kelvin Waves Over Tropical South America

AbstractRainfall over tropical South America is known to be modulated by convectively coupled Kelvin waves (CCKWs). In this work, the origin and dynamical features of South American Kelvin waves are revisited using satellite-observed brightness temperature, radiosonde, and reanalysis datasets. Two main types of CCKWs over the Amazon are considered: Kelvin waves with a Pacific precursor, and Kelvin waves with a precursor originating over South America. Amazonian CCKW’s associated with a preexisting Kelvin convection in the eastern Pacific account for about 35% of the total events. The cases with South American precursors are associated with either pressure surges in the western Amazon from extratropical wave train activity, responsible for 40% of the total events, or “in situ” convection that locally excites CCKWs, accounting for the remaining 25%. The analysis also suggests that CCKWs associated with different precursors are sensitive to Pacific sea surface temperature. Kelvin wave events with a Pacific precursor are more common during ENSO warm events, while Kelvin waves with extratropical South American precursors show stronger activity during La Niña events. This study also explores other triggering mechanisms of CCKWs over the Amazon. These mechanisms are associated with: 1) extratropical Rossby wave trains not necessarily of extratropical South American origin; 2) CCKWs initiated in response to the presence of the southern and/or double Intertropical Convergence Zone (ITCZ) in the Eastern Pacific Ocean; 3) and possible interaction between CCKWs and other equatorial waves in the Amazon.

The application of a hybrid sea surface temperature algorithm to COMS geostationary satellite data in the Northwest Pacific Ocean

ABSTRACT The hybrid sea surface temperature (SST) algorithm derives coefficients for SST estimation based on the regression procedure of incremental values between the satellite-observed brightness temperature (BT) minus the first-guess BT and the in-situ temperature minus the first-guess SST. In this study, the hybrid SST algorithm was applied to retrieve the SST coefficients of which the results were compared with extensively used regression algorithms of Multi-Channel SST (MCSST) and Non-Linear SST (NLSST) using data obtained by the Meteorological Imager of the first Korean geostationary satellite, the Communication, Ocean and Meteorological Satellite, in the Northwest Pacific Ocean in May 2014. The empirical regression SSTs and hybrid SSTs exhibited similar root-mean-squared errors in the range of 0.61–0.84°C. Although the SST errors were quite similar to each other, the comparison of the hybrid SSTs with the in-situ SSTs indicated a slightly higher accuracy of approximately 0.2°C compared with that of the empirically derived SSTs. SST retrieval using the hybrid algorithm reduced the errors near the cloud edges. The differences between the hybrid SST and regression SST tended to be greater when the distance to the nearest cloudy pixel was less than 0.2°. This study discusses the potential causes of the differences between the SST errors of the algorithms and addresses a few constraints in the execution of the hybrid algorithm related to the numerical model.

A Unique Satellite-Based Sea Surface Wind Speed Algorithm and Its Application in Tropical Cyclone Intensity Analysis

AbstractThis study proposes a sea surface wind speed retrieval algorithm (the Hong wind speed algorithm) for use in rainy and rain-free conditions. It uses a combination of satellite-observed microwave brightness temperatures, sea surface temperatures, and horizontally polarized surface reflectivities from the fast Radiative Transfer for TOVS (RTTOV), and surface and atmospheric profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF). Regression relationships between satellite-observed brightness temperature and satellite-simulated brightness temperatures, satellite-simulated brightness temperatures, rough surface reflectivities, and between sea surface roughness and sea surface wind speed are derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2). Validation results of sea surface wind speed between the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 and 1.21 m s−1, respectively. Typhoon intensities such as the current intensity (CI) number, maximum wind speed, and minimum pressure level based on the proposed technique (the Hong technique) are compared with best-track data from the Japan Meteorological Agency (JMA), the Joint Typhoon Warning Center (JTWC), and the Cooperative Institute for Mesoscale Meteorological Studies (CIMSS) for 13 typhoons that occurred in the northeastern Pacific Ocean throughout 2012. Although the results show good agreement for low- and medium-range typhoon intensities, the discrepancy increases with typhoon intensity. Consequently, this study provides a useful retrieval algorithm for estimating sea surface wind speed, even during rainy conditions, and for analyzing characteristics of tropical cyclones.

Identification of clay pans from AMSR-E passive microwave observations

A new methodology to derive the spatial distribution of clay pans from satellite microwave data is presented. Soil moisture has a different temporal signature in clay pans compared with other soils, which is directly reflected in the satellite-observed brightness temperatures. Three years of Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) 6.9 GHz microwave observations were compiled and analysed over continental Australia to identify clay pans. This led to the development of a brightness temperature variance index (BTVI), which shows a strong spatial correspondence to an existing soil texture map and the ability to map clay pans for semi-arid regions. This simple method emphasizes the potential use of passive microwave remote sensing for soil type mapping.

Climatology and Interannual Variability of Convectively Coupled Equatorial Waves Activity

Abstract Climatology and interannual variability of convectively coupled equatorial wave (CCEW) activity, including the mixed Rossby–gravity (MRG), tropical-depression-type (TD-type), equatorial Rossby (ER), and Kelvin waves, are investigated using the satellite-observed brightness temperature data from the Cloud Archive User Service. The monthly activity of CCEWs is represented by the root mean square of the daily filtered convections in each month based on the Wheeler–Kiladis filtering method. More precise seasonal cycles of CCEW activity are obtained from the meridional and zonal mean climatology. Interannual variance of CCEW activity is further investigated. Kelvin wave activity has maximum interannual variance over the eastern Pacific, while the other three waves are most variable in the intertropical convergence zone. The four active CCEWs all have significant correlation with the background convection and local sea surface temperature (SST) over the central and eastern Pacific, but they are not significantly correlated over other regions. The El Niño events may induce more trapped and active CCEWs over the central and eastern Pacific but weaker MRG and TD-type waves over the warm pool. In contrast, the El Niño Modoki has much weaker correlation with CCEW activity. CCEW activity over the southeastern Indian Ocean is negatively correlated with the Indian Ocean dipole, while that over the western and northern Indian Ocean may be determined by atmospheric internal disturbances. The tropical southern Atlantic mode is the strongest Atlantic SST anomaly mode correlated with the Atlantic CCEW activity.

Convectively Coupled Kelvin Waves over Tropical Africa during the Boreal Summer: Structure and Variability

Abstract The structure and variability of convectively coupled Kelvin waves during the boreal summer are explored using satellite-observed brightness temperature data and ECMWF reanalyses. Kelvin wave activity is most prominent between the central and eastern Pacific, across Africa, and the Indian Ocean. Composite analysis shows that over sub-Saharan Africa Kelvin wave convection is peaked north of the equator, while the dynamical fields tend to be symmetric with respect to the equator. Convectively coupled Kelvin waves propagate faster over the Pacific and western Atlantic (∼24 m s−1), and slow down over tropical Africa (∼14 m s−1), consistent with stronger coupling between the dynamics and convection over tropical Africa. The Kelvin waves observed over Africa generally propagate into the region from anywhere between the eastern Pacific and the Atlantic, and decay over the eastern Indian Ocean basin. Results show marked interannual variability of Kelvin wave activity over Africa. Anomalously high Kelvin wave variance tends to occur during dry years, while low variance occurs during wet years. African Kelvin wave activity is positively correlated with SST anomalies in the equatorial east Pacific. The same warm SST anomalies that are favorable for enhanced Kelvin wave activity suppress the mean rainfall over tropical Africa via a more slowly varying teleconnection and associated subsidence. A brief analysis of an intense Kelvin wave in August 1987 (a dry year) shows a clear impact of the wave on convective development and daily rainfall over tropical Africa. This Kelvin wave was also associated with a series of easterly wave initiations over tropical Africa.

Convectively Coupled Equatorial Waves. Part I: Horizontal and Vertical Structures

Abstract Multilevel 15-yr ECMWF Re-Analysis (ERA-15) and satellite-observed brightness temperature (Tb) data for the period May–October 1992 are used to examine the horizontal and vertical structures of convectively coupled equatorial waves. Dynamical waves are isolated using a methodology developed previously. Composite structures of convectively coupled equatorial waves are obtained using linear regression/correlation between convection (Tb) and dynamical structures. It is found that the relationship depends on the ambient flow and the nature of the convective coupling, and varies between off-equatorial- and equatorial-centered convection, different hemispheres, and seasons. The Kelvin wave structure in the Western Hemisphere is generally consistent with classic equatorial wave theory and has its convection located in the region of low-level convergence. In the Eastern Hemisphere the Kelvin wave tends to have convection in the region of enhanced lower-tropospheric westerlies and a tilted vertical structure. The Kelvin wave also tends to have a third peak in zonal wind amplitude at 500 hPa and exhibits upward propagation into the lower stratosphere. Lower-tropospheric westward-moving mixed Rossby–gravity (WMRG) and n = 1 Rossby (R1) wave structures and their relationship with convection are consistent with classic equatorial wave theory and the implied lower-tropospheric convergences. In the Eastern Hemisphere the WMRG and R1 waves have first baroclinic mode structures in the vertical. However, in the Western Hemisphere, the R1 wave has a barotropic structure. In the Eastern Hemisphere the R1 wave, like the Kelvin wave, tends to have equatorial convection in the region of enhanced lower-level westerlies, suggesting that enhanced surface energy fluxes associated with these waves may play an important organizing role for equatorial convection in this warm-water hemisphere. In the upper troposphere, eastward-moving Rossby–gravity (EMRG) and n = 1 gravity waves are found in the Eastern Hemisphere, and eastward-moving WMRG and R1 waves are found in the Western Hemisphere, suggestive of Doppler shifting of waves by the ambient flow.

Radiative Transfer Simulations Using Mesoscale Cloud Model Outputs: Comparisons with Passive Microwave and Infrared Satellite Observations for Midlatitudes

AbstractReal midlatitude meteorological cases are simulated over western Europe with the cloud mesoscale model Méso-NH, and the outputs are used to calculate brightness temperatures at microwave frequencies with the Atmospheric Transmission at Microwave (ATM) radiative transfer model. Satellite-observed brightness temperatures (TBs) from the Advanced Microwave Scanning Unit B (AMSU-B) and the Special Sensor Microwave Imager (SSM/I) are compared to the simulated ones. In this paper, one specific situation is examined in detail. The infrared responses have also been calculated and compared to the Meteosat coincident observations. Overall agreement is obtained between the simulated and the observed brightness temperatures in the microwave and in the infrared. The large-scale dynamical structure of the cloud system is well captured by Méso-NH. However, in regions with large quantities of frozen hydrometeors, the comparison shows that the simulated microwave TBs are higher than the measured ones in the window channels at high frequencies, indicating that the calculation does not predict enough scattering. The factors responsible for the scattering (frozen particle distribution, calculation of particle dielectric properties, and nonsphericity of the particles) are analyzed. To assess the quality of the cloud and precipitation simulations by Méso-NH, the microphysical fields predicted by the German Lokal-Modell are also considered. Results show that in these midlatitude situations, the treatment of the snow category has a high impact on the simulated brightness temperatures. The snow scattering parameters are tuned to match the discrete dipole approximation calculations and to obtain a good agreement between simulations and observations even in the areas with significant frozen particles. Analysis of the other meteorological simulations confirms these results. Comparing simulations and observations in the microwave provides a powerful evaluation of resolved clouds in mesoscale models, especially the precipitating ice phase.

Adaptation of a model‐generated cloud database to satellite observations

Cloud resolving model outputs are often used to build databases for satellite microwave remote sensing of precipitating clouds. A known problem of this approach is that cloud resolving models tend to systematically produce excessive amount of high density frozen hydrometeors, causing the cloud/radiation model database to have stronger scattering signatures at high microwave frequencies than those observed by satellite or airborne sensors. Consequently, it lowers the performance of the cloud and precipitation retrieval algorithms that utilize the model database. Since multi‐frequency satellite observations contain information on hydrometeors' properties, measured brightness temperatures can give guidance as to how the modeled cloud database may be modified to better mimic natural clouds. Following this philosophy, in this study, we propose a method to adapt the modeled database toward observations. The newly constructed database results in a better match to the characteristics of the satellite observed brightness temperatures.

Sampling strategies for the comparison of climate model calculated and satellite observed brightness temperatures

Brightness temperatures derived from polar‐orbiting satellites are valuable for the evaluation of global climate models. However, the effect of orbital constraints must be taken into account to ensure valid comparisons. As part of the Atmospheric Model Intercomparison Project II climate model comparisons, this study seeks to evaluate the monthly mean simulated brightness temperature differences of possible model output sampling strategies with respect to the exact satellite sampling and whether they can be practically implemented to provide meaningful comparisons with these satellite observations. We compare various sampling strategies with a proxy satellite data set constructed from model output and actual TIROS operational vertical sounder orbital trajectories, rather than with the observations themselves. To a large extent, this enables isolation of the sampling error from errors caused by deficiencies in the modeled climate processes. Our results suggest that the traditional method of calculating brightness temperatures from monthly mean temperature and moisture profiles yields biases from both nonlinear effects and the removal of the diurnal cycle that may be unacceptable in many applications. However, we also find that a brightness temperature calculation every hour of the simulation provides substantially lower sampling biases provided that there are two or more properly aligned satellites. This is encouraging because it means that for many applications modelers need not accurately mimic actual satellite trajectories in the sampling of their simulations. If only one satellite is available for comparison with simulations, more sophisticated sampling seems necessary. For such circumstances, we introduce a simple procedure that serves as a useful approximation to the rather complex procedure required to sample a model exactly as a polar‐orbiting satellite does the Earth. With all sampling methods, removal of biases associated with cloud cover is problematic and deserves further study.

Global Data Assimilation and Forecast Experiments Using SSM/I Wind Speed Data Derived from a Neural Network Algorithm

A neural network algorithm used in this study to derive Special Sensor Microwave/Imager (SSM/I) wind speeds from the Defense Meteorological Satellite Program satellite-observed brightness temperatures is briefly reviewed. The SSM/I winds derived from the neural network algorithm are not only of better quality, but also cover a larger area when compared to those generated from the currently operational Goodberlet algorithm. The areas of increased coverage occur mainly over the regions of active weather developments where the operational Goodberlet algorithm fails to produce good quality wind data due to high moisture contents of the atmosphere. These two main characteristics associated with the SSM/I winds derived from the neural network algorithm are discussed. SSM/I wind speed data derived from both the neural network algorithm and the operational Goodberlet algorithm are tested in parallel global data assimilation and forecast experiments for a period of about three weeks. The results show that the use of neural-network-derived SSM/I wind speed data leads to a greater improvement in the first-guess wind fields than use of wind data generated by the operational algorithm. Similarly, comparison of the forecast results shows that use of the neural-network-derived SSM/I wind speed data in the data assimilation and forecast experiment gives better forecasts when compared to those from the operational run that uses the SSM/I winds from the Goodberlet algorithm. These results of comparison between the two parallel analyses and forecasts from the global data assimilation experiments are discussed.

Selection method of initial guess values for surface temperature estimation from the satellite data

Abstract For the estimation of sea surface temperature on the basis of linearized inversion of the radiative transfer equation (Rodgers method), the selection method of the initial guess values of sea surface temperature and atmospheric condition is proposed. The selection method uses the temporal statistics of air temperature profile and precipitable water profiles as an atmospheric models. The satellite observed brightness temperature is calculated from the atmospheric models and some surface models, and the atmospheric model and surface model which makes the difference between the calculated and the observed radiance minimize is selected as the initial guess values. The sea surface temperature estimation from MOS-1/VTIR data shows a good correspondence to the sea truth data.

Microwave vegetation optical depth and inverse modelling of soil emissivity using Nimbus/SMMR satellite observations

A radiative transfer model has been used to determine the large scale effective 6.6 GHz and 37 GHz optical depths of the vegetation cover. Knowledge of the vegetation optical depth is important for satellite-based large scale soil moisture monitoring using microwave radiometry. The study is based on actual observed large scale surface soil moisture data and observed dual polarization 6.6 and 37 GHz Nimbus/SMMR brightness temperatures over a 3-year period. The derived optical depths have been compared with microwave polarization differences and polarization ratios in both frequencies and with Normalized Difference Vegetation Index (NDVI) values from NOAA/AVHRR. A synergistic approach to derive surface soil emissivity from satellite observed brightness temperatures by inverse modelling is described. This approach improves the relationship between satellite derived surface emissivity and large scale top soil moisture fromR 2=0.45 (no correction for vegetation) toR 2=0.72 (after correction for vegetation). This study also confirms the relationship between the microwave-based MPDI and NDVI earlier described and explained in the literature.

Operational Cloud-Motion Winds from Meteosat Infrared Images

The displacement of clouds in successive satellite images reflects the atmospheric circulation at various scales. The main application of the satellite-derived cloud-motion vectors is their use as winds in the data analysis for numerical weather prediction. At low latitudes in particular they constitute an indispensible data source for numerical weather prediction. This paper describes the operational method of deriving cloud-motion winds (CMW) from the IR image (10.5–12.5 µm) of the European geostationary Meteostat satellites. The method is automatic, that is, the cloud tracking uses cross correlation and the height assignment is based on satellite observed brightness temperature and a forecast temperature profile. Semitransparent clouds undergo a height correction based on radiative forward calculations and simultaneous radiance observations in both the IR and water vapor (5.7–7.1 µm) channel. Cloud-motion winds are subject to various quality checks that include manual quality control as the last step. Typically about 3000 wind vectors are produced per day over four production cycles. This paper documents algorithm changes and improvements made to the operational CMWs over the last five years. The improvements are shown by long-term comparisons with both collocated radiosondes and the first guess of the forecast model of the European Centre for Medium-Range Weather Forecasts. In particular, the height assignment of a wind vector and radiance filtering techniques preceding the cloud tracking have ameliorated the errors in Meteostat winds. The slow speed bias of high-level CMWs (<400 hPa) in comparison to radiosonde winds have been reduced from about 4 to 1.3 m s−1 for a mean wind speed of 24 m s−1. Correspondingly, the rms vectors error of Meteosat high-level CMWs decreased from about 7.8 to 5 m s−1. Medium- and low-level CMWs were also significantly improved.

Influence of cirrus clouds on the VISSR atmospheric sounder-derived sea surface temperature determinations

Using a more realistic cirrus cloud model, the characteristics of transmittance, emittance, and optical thickness and their relationships to cirrus in a diverse set of cases are studied by solving the equation of transfer of IR radiation. The doubling method is employed in the multiple scattering calculation. The satellite-observed brightness temperatures for different cases are computed, and stepwise regression analyses are performed to yield retrieval equations for sea surface temperature (SST). It is shown that the radiative properties of cirrus depend strongly on particle concentration, thus on the optical thickness of clouds. For clear atmospheres, channel 8 (11.2 microm) is more transparent than other channels. For cirrus clouds only, when the optical thickness of cirrus tau(c) is <0.10, channel 8 is still more transparent, while, with tau(c) increasing from 0.2 to between 4 and 8, channel 12 (4 microm) becomes the most transparent. When tau(c) >/= 8, the transparency of channel 12 decreases and those of other channels increase. For a very large r, the transparency of VAS channels will become almost equal. In addition, the IR absorption emittance of cirrus and the brightness temperatures also have sensitivities to different cloud optical thicknesses. The general retrieval equation for the determinations of SST, which is suitable for the clear air model as well as for the cirrus cloud atmospheres (with our definition of cirrus), is obtained through a combination of channels 12, 8, 6 (4.5 microm), and 5 (13.3 microm).The retrieval error is <1.0 K. The error analyses indicate that the clear air retrieval equations should not be used for SST determination in cirrus conditions.

A Satellite Passive 37-GHz Scattering-based Method for Measuring Oceanic Rain Rates

A combination of theory and measurement is used to develop a scattering-based method for quantitatively measuring rainfall over the ocean from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) 37-GHz observations. This technique takes the observed scattering effects of precipitation on 37-GHz brightness temperatures and applies it to the oceanic environment. It requires an estimate of the effective radiating temperature of the cloudy portion of the atmosphere, and a brightness temperature measurement of the cloud-free ocean surface. These two measurements bound all possible combinations of clear and cloudy conditions within a footprint in terms of bipolarized brightness temperatures. Any satellite-observed brightness temperature T(B) lower than these values is assumed to reflect scattering, which at 37 GHz is only due to precipitation-size hydrometeors. Because the technique involves linear transformation between dual polarized brightness temperature and rain rate, there are no nonlinear 'footprint filling' effects and a unique footprint-averaged rain rate results. It is shown that these SMMR-derived rain rates for five cases of convection over the Gulf of Mexico are closely related to simultaneously derived radar rain rates, having a correlation of 0.90. This technique is then applied to a massive squall line over the Gulf of Mexico, and the resulting rain rate distribution reflects features found in cloud top heights and texture inferred from GOES infrared and visible imagery.