Sid-Ahmed Boukabara

Radiance and Jacobian Intercomparison of Radiative Transfer Models Applied to HIRS and AMSU Channels

The goals of this study are the evaluation of current fast radiative transfer models (RTMs) and line-by-line (LBL) models. The intercomparison focuses on the modeling of 11 representative sounding channels routinely used at numerical weather prediction centers: 7 HIRS (High-resolution Infrared Sounder) and 4 AMSU (advanced microwave sounding unit) channels. Interest in this topic was evident by the participation of 24 scientists from 16 institutions. An ensemble of 42 diverse atmospheres was used and results compiled for 19 infrared models and 10 microwave models, including several LBL RTMs. For the first time, not only radiances but also Jacobians (of temperature, water vapor, and ozone) were compared to various LBL models for many channels. In the infrared, LBL models typically agree to within 0.05-0.15 K (standard deviation) in terms of top-of-the-atmosphere brightness temperature (BT). Individual differences up to 0.5 K still exist, systematic in some channels, and linked to the type of atmosphere in others. The best fast models emulate LBL BTs to within 0.25 K, but no model achieves this desirable level of success for all channels. The ozone modeling is particularly challenging. In the microwave, fast models generally do quite well against the LBL model to which they were tuned. However, significant differences were noted among LBL models. Extending the intercomparison to the Jacobians proved very useful in detecting subtle or more obvious modeling errors. In addition, total and single gas optical depths were calculated, which provided additional insight on the nature of differences.

The effect of the half-width of the 22-GHz water vapor line on retrievals of temperature and water vapor profiles with a 12-channel microwave radiometer

We show that observed biases in retrievals of temperature and water vapor profiles from a 12-channel microwave radiometer arise from systematic differences between the observed and model-calculated brightness temperatures at five measurement frequencies between 22 and 30 GHz. Replacing the value for the air-broadened half-width of the 22-GHz water vapor line used in the Rosenkranz absorption model with the 5% smaller half-width from the HITRAN compilation largely eliminated the systematic differences in brightness temperatures. An a priori statistical retrieval based on the revised model demonstrated significant improvements in the accuracy and vertical resolution of the retrieved temperature and water vapor profiles. Additional improvements were demonstrated by combining the MWRP retrievals with those from the GOES-8 sounder and by incorporating brightness temperature measurements at off-zenith angles in the retrievals.

MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System

A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products.

An Evaluation of Microwave Land Surface Emissivities Over the Continental United States to Benefit GPM-Era Precipitation Algorithms

Passive microwave (PMW) satellite-based precipitation over land algorithms rely on physical models to define the most appropriate channel combinations to use in the retrieval, yet typically require considerable empirical adaptation of the model for use with the satellite measurements. Although low-frequency channels are better suited to measure the emission due to liquid associated with rain, most techniques to date rely on high-frequency, scattering-based schemes since the low-frequency methods are limited to the highly variable land surface background, whose radiometric contribution is substantial and can vary more than the contribution of the rain signal. Thus, emission techniques are generally useless over the majority of the Earths surface. As a first step toward advancing to globally useful physical retrieval schemes, an intercomparison project was organized to determine the accuracy and variability of several emissivity retrieval schemes. A three-year period (July 2004-June 2007) over different targets with varying surface characteristics was developed. The PMW radiometer data used includes the Special Sensor Microwave Imagers, SSMI Sounder, Advanced Microwave Scanning Radiometer (AMSR-E), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Advanced Microwave Sounding Units, and Microwave Humidity Sounder, along with land surface model emissivity estimates. Results from three specific targets in North America were examined. While there are notable discrepancies among the estimates, similar seasonal trends and associated variability were noted. Because of differences in the treatment surface temperature in the various techniques, it was found that comparing the product of temperature and emissivity yielded more insight than when comparing the emissivity alone. This product is the major contribution to the overall signal measured by PMW sensors and, if it can be properly retrieved, will improve the utility of emission techniques for over land precipitation retrievals. As a more rigorous means of comparison, these emissivity time series were analyzed jointly with precipitation data sets, to examine the emissivity response immediately following rain events. The results demonstrate that while the emissivity structure can be fairly well characterized for certain surface types, there are other more complex surfaces where the underlying variability is more than can be captured with the PMW channels. The implications for Global Precipitation Measurement-era algorithms suggest that physical retrievals are feasible over vegetated land during the warm seasons.

Passive Microwave Remote Sensing of Extreme Weather Events Using NOAA-18 AMSUA and MHS

The ability to provide temperature and water-vapor soundings under extreme weather conditions, such as hurricanes, could extend the coverage of space-based measurements to critical areas and provide information that could enhance outcomes of numerical weather prediction (NWP) models and other storm-track forecasting models, which, in turn, could have vital societal benefits. An NWP-independent 1D-VAR system has been developed to carry out the simultaneous restitutions of atmospheric constituents and surface parameters in all weather conditions. This consistent treatment of all components that have an impact on the measurements allows an optimal information-content extraction. This study focuses on the data from the NOAA-18 satellite (AMSUA and MHS sounders). The retrieval of the precipitating and nonprecipitating cloud parameters is done in a profile form, taking advantage of the natural correlations that do exist between the different parameters and across the vertical layers. Stability and the problems ill-posed nature are the two classical issues facing this type of retrieval. The use of empirically orthogonal-function decomposition leads to a dramatic stabilization of the problem. The main goal of this inversion system is to be able to retrieve independently, with a high-enough accuracy and under all conditions, the temperature and water-vapor profiles, which are still the two main prognostic variables in numerical weather forecast models. Validation of these parameters in different conditions is undertaken in this paper by comparing the case-by-case retrievals with GPS-dropsondes data and NWP analyses in and around a hurricane. High temporal and spatial variabilities of the atmosphere are shown to present a challenge to any attempt to validate the microwave remote-sensing retrievals in meteorologically active areas.

Assessment of a Variational Inversion System for Rainfall Rate Over Land and Water Surfaces

A comprehensive system that is used to invert the geophysical products from microwave measurements has recently been developed. This system, known as the Microwave Integrated Retrieval System (MiRS), ensures that the final solution is consistent with the measurements and, when used as input to the forward operator, fits them to within the instrument noise levels. In the presence of precipitation, this variational algorithm retrieves a set of hydrometeor products consisting of cloud liquid water, ice water, and rain water content profiles. This paper presents the development and assessment of the MiRS rainfall rate that is derived based on a predetermined relationship of the rainfall with these hydrometeor products. Since this relationship relies on the geophysical products retrieved by the MiRS as inputs and not on sensor-dependent parameters, the technique is suitable for all microwave sensors to which the MiRS is applied. This precipitation technique has been designed to facilitate its transition from research to operations when applied to current and future satellite-based sensors. Currently, the MiRS rainfall rate technique has been implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) for the NOAA-18, NOAA-19, Metop-A Advanced Microwave Sounding Unit, and Microwave Humidity Sensor, as well as for the Defense Meteorological Satellite Program (DMSP)-F16 and DMSP-F18 Special Sensor Microwave Imager/Sounder microwave satellite sensors. For the validation of the MiRS rainfall rate technique, extensive comparisons with state-of-the-art precipitation products derived from rain gauge, ground-based radar, and satellite-based microwave observations are presented for different regions and seasons, and over land and ocean. The MiRS rainfall rate technique is shown to estimate precipitation, with a skill comparable to other satellite-based microwave precipitation algorithms, including the MSPPS, 3B40RT, and MWCOMB, while showing no discontinuities at coasts. This is a relevant result, considering that the MiRS is a system not merely designed to retrieve the rainfall rate but to consistently estimate a comprehensive set of atmospheric and surface parameters from microwave measurements.

Global Coverage of Total Precipitable Water Using a Microwave Variational Algorithm

This study introduces a variational approach to retrieve total precipitable water (TPW) over all surface backgrounds including ocean, land, snow, sea-ice, and coastal areas, from microwave sensors. The product has been used routinely by forecasters since its recent operational implementation. The emissivity is accounted for by including its spectrum within the retrieved state vector, which allows for a pixel-to-pixel variation of the emissivity, a factor usually preventing the TPW retrieval over land. The algorithm, implemented operationally at the National Atmospheric and Oceanic Administration (NOAA), is called the Microwave Integrated Retrieval System (MiRS). Its main characteristic, besides its applicability over all surfaces, is its validity under all weather conditions. With a generic design, the algorithm is being applied to the following microwave sensors: (1) AMSU and MHS onboard NOAA-18; (2) NOAA-19 and Metop-A; as well as (3) SSMI/S onboard DMSP-F16 platform. The assessment of the MiRS performances is done by undertaking extensive comparisons to the National Center for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses, to a network of radiosondes and to existing well-established algorithms over ocean, encompassing a wide variety of meteorological situations. The performance of MiRS TPW is shown to depend on the sensor, the reference data source as well as on the surface background considered. It is shown to behave quite well over all surfaces and in all weather conditions, except when there is rain. Although this study focuses on the retrieval of TPW with an emphasis on non-oceanic surfaces, the underlying application of this study is the potential improvement in the variational data assimilation of Numerical Weather Prediction (NWP) models. Indeed, the same dynamic approach could be employed in order to assimilate more surface-sensitive microwave channels, over a multitude of surfaces.

A New Sea-Ice Concentration Algorithm Based on Microwave Surface Emissivities—Application to AMSU Measurements

Passive microwave sea-ice retrieval algorithms are typically tuned to brightness temperature measurements with simple treatments of weather effects. The new technique presented is a two-step algorithm that variationally retrieves surface emissivities from microwave remote sensing observations, followed by the retrieval of sea-ice concentration from surface emissivities. Surface emissivity spectra are interpreted for determining sea-ice fraction by comparison with a catalog of sea-ice emissivities to find the closest match. This catalog was computed off-line from known ocean, first-year, and multiyear sea-ice reference emissivities for a range of fractions. The technique was adjusted for application to the Advanced Microwave Sounding Unit (AMSU)/Microwave Humidity Sensor observations, and its performance was compared to the National Oceanic and Atmospheric Administration (NOAA)s AMSU heritage sea-ice algorithm and to NOAAs operational Interactive Multi-sensor Snow and Ice Mapping System taken as ground truth. Assessment results indicate a performance that is superior to the heritage algorithm particularly over multiyear ice and during the warm season.

Evaluation of modeled microwave land surface emissivities with satellite-based estimates

An accurate estimate of the microwave surface emissivity is necessary for the retrieval of atmospheric quantities from microwave imagers or sounders. The objective of this study is to evaluate the microwave land surface emissivity modeling of the Community Radiative Transfer Model (CRTM), providing quantitative statistic information for further model improvements. First, the model-simulated emissivity is compared to emissivity estimates derived from satellite observations (TELSEM, Tool to Estimate Land Surface Emissivities at Microwaves). The model simulations agree reasonably well with TELSEM over snow-free vegetated areas, especially at vertical polarization up to 40 GHz. For snow-free surfaces, the mean difference between CRTM and TELSEM emissivities at vertical polarization is lower than 0.01 below 40 GHz and increases to 0.02 at 89 GHz. At horizontal polarization, it increases with frequency, from 0.01 at 10.6 GHz to 0.04 at 89 GHz. Over deserts and snow, larger differences are observed, which can be due to the lack of quality inputs to the model in these complex environments. A further evaluation is provided by comparing brightness temperature (Tbs) simulations with AMSR-E observations, where CRTM emissivity and TELSEM emissivity are coupled into a comprehensive radiative transfer model to simulate the brightness temperatures, respectively. The comparison shows smaller RMS errors with the satellite-derived estimates than with the model, despite some significant bias at midday with the satellite-derived emissivities at high frequencies. This study confirms and extends to the global scale previous evaluations of land surface microwave emissivity model. It emphasizes the needs for better physical modeling in arid regions and over snow-covered surfaces.

Microwave Emissivity Over Ocean in All-Weather Conditions: Validation Using WINDSAT and Airborne GPS Dropsondes

Emissivity spectra computed by the FASTEM-3 model using global positioning system dropsonde wind as input are compared to emissivity retrieved from stringently coincident WINDSAT measurements, using a variational approach. This is done in both clear and rainy conditions to assess the validity of both the emissivity model and the retrieval technique in all conditions. Results of this comparison are presented for vertical and horizontal polarizations, in moderate to high wind conditions. In particular, a slope difference is found, and its potential sources are discussed in this paper.