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Fatima Karbou, Florence Rabier, and Catherine Prigent

Abstract

The aim of this study is to test the feasibility of assimilating microwave observations from the Advanced Microwave Sounding Units (AMSU-A and AMSU-B) through the implementation of an appropriate parameterization of sea ice emissivity. AMSU observations are relevant to the description of air temperature and humidity, and their assimilation into numerical weather prediction (NWP) helps better constrain models in regions where very few observations are assimilated. A sea ice emissivity model suitable for AMSU-A and AMSU-B data is described in this paper and its impact is studied through two assimilation experiments run during the period of the Arctic winter. The first experiment is representative of the operational version of the Météo-France NWP model whereas the second simulation uses the sea ice emissivity parameterization and assimilates a selection of AMSU channels above polar regions. The assimilation of AMSU observations over sea ice is shown to have a significant effect on atmospheric analyses (in particular those of temperature and humidity). The effect on temperature induces a warming in the lower troposphere, especially around 850 hPa. This leads to an increase in the Arctic inversion strength over the ice cap by almost 2 K. An improvement in medium-range forecasts is also noticed when the NWP model assimilates AMSU observations over sea ice.

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Catherine Prigent, Filipe Aires, and William B. Rossow

Microwave land surface emissivities have been calculated over the globe for ~10 yr between 19 and 85 GHz at 53° incidence angle for both orthogonal polarizations, using satellite observations from the Special Sensor Microwave Imager (SSM/I). Ancillary data (IR satellite observations and meteorological reanalysis) help remove the contribution from the atmosphere, clouds, and rain from the measured satellite signal and separate surface temperature from emissivity variations. The method to calculate the emissivity is general and can be applied to other sensors. The monthly mean emissivities are available for the community, with a 0.25° × 0.25° spatial resolution.

The emissivities are sensitive to variations of the vegetation density, the soil moisture, the presence of standing water at the surface, or the snow behavior, and can help characterize the land surface properties.

These emissivities (not illustrated in this paper) also allow for improved atmospheric retrieval over land and can help evaluate land surface emissivity models at global scales.

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Filipe Aires, Fabrice Papa, and Catherine Prigent

Abstract

A climatology of wetlands has been derived at a low spatial resolution (0.25° × 0.25° equal-area grid) over a 15-yr period by combining visible and near-infrared satellite observations and passive and active microwaves. The objective of this study is to develop a downscaling technique able to retrieve wetland estimations at a higher spatial resolution (about 500 m). The proposed method uses an image-processing technique applied to synthetic aperture radar (SAR) information about the low and high wetland season. This method is tested over the densely vegetated basin of the Amazon. The downscaling results are satisfactory since they respect the spatial hydrological features of the SAR data and the temporal evolution of the low-resolution wetland estimates. A new long-term and high-resolution wetland dataset has been generated for 1993–2007 for the Amazon basin. This dataset represents a new and unprecedented source of information for climate and land surface modeling of the Amazon and for the definition of future hydrology-oriented satellite missions such as Surface Water and Ocean Topography (SWOT).

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Filipe Aires, Frédéric Bernardo, Héléne Brogniez, and Catherine Prigent

Abstract

Retrieval schemes often use two important components: 1) a radiative transfer model (RTM) inside the retrieval procedure or to construct the learning dataset for the training of the statistical retrieval algorithms and 2) a numerical weather prediction (NWP) model to provide a first guess or, again, to construct a learning dataset. This is particularly true in operational centers. As a consequence, any physical retrieval or similar method is limited by inaccuracies in the RTM and NWP models on which it is based. In this paper, a method for partially compensating for these errors as part of the sensor calibration is presented and evaluated. In general, RTM/NWP errors are minimized as best as possible prior to the training of the retrieval method, and then tolerated. The proposed method reduces these unknown and generally nonlinear residual errors by training a separate preprocessing neural network (NN) to produce calibrated radiances from real satellite data that approximate those radiances produced by the “flawed” NWP and RTM models. The final “compensated/flawed” retrieval assures better internal consistency of the retrieval procedure and then produces more accurate results. To the authors’ knowledge, this type of NN model has not been used yet for this purpose. The calibration approach is illustrated here on one particular application: the retrieval of atmospheric water vapor from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Humidity Sounder for Brazil (HSB) measurements for nonprecipitating scenes, over land and ocean. Before being inverted, the real observations are “projected” into the space of the RTM simulation space from which the retrieval is designed. Validation of results is performed with radiosonde measurements and NWP analysis departures. This study shows that the NN calibration of the AMSR-E/HSB observations improves water vapor inversion, over ocean and land, for both clear and cloudy situations. The NN calibration is efficient and very general, being applicable to a large variety of problems. The nonlinearity of the NN allows for the calibration procedure to be state dependent and adaptable to specific cases (e.g., the same correction will not be applied to medium-range measurement and to extreme conditions). Its multivariate nature allows for a full exploitation of the complex correlation structure among the instrument channels, making the calibration of each single channel more robust. The procedure would make it possible to project the satellite observations in a reference observational space defined by radiosonde measurements, RTM simulations, or other instrument observational space.

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Binh Pham-Duc, Catherine Prigent, Filipe Aires, and Fabrice Papa

Abstract

Continental surface water extents and dynamics are key information to model Earth’s hydrological and biochemical cycles. This study presents global and regional comparisons between two multisatellite surface water extent datasets, the Global Inundation Extent from Multi-Satellites (GIEMS) and the Surface Water Microwave Product Series (SWAMPS), for the 1993–2007 period, along with two widely used static inundation datasets, the Global Lakes and Wetlands Database (GLWD) and the Matthews and Fung wetland estimates. Maximum surface water extents derived from these datasets are largely different: ~13 × 106 km2 from GLWD, ~5.3 × 106 km2 from Matthews and Fung, ~6.2 × 106 km2 from GIEMS, and ~10.3 × 106 km2 from SWAMPS. SWAMPS global maximum surface extent reduces by nearly 51% (to ~5 × 106 km2) when applying a coastal filter, showing a strong contamination in this retrieval over the coastal regions. Anomalous surface waters are also detected with SWAMPS over desert areas. The seasonal amplitude of the GIEMS surface waters is much larger than the SWAMPS estimates, and GIEMS dynamics is more consistent with other hydrological variables such as the river discharge. Over the Amazon basin, GIEMS and SWAMPS show a very high time series correlation (95%), but with SWAMPS maximum extent half the size of that from GIEMS and from previous synthetic aperture radar estimates. Over the Niger basin, SWAMPS seasonal cycle is out of phase with both GIEMS and MODIS-derived water extent estimates, as well as with river discharge data.

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Filipe Aires, Francis Marquisseau, Catherine Prigent, and Geneviève Sèze

Abstract

A statistical cloud classification and cloud mask algorithm is developed based on Advanced Microwave Sounding Unit (AMSU-A and -B) microwave (MW) observations. The visible and infrared data from the Meteosat Third Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) are used to train the microwave classifier. The goal of the MW algorithms is not to fully reproduce this MSG-SEVIRI cloud classification, as the MW observations do not have enough information on clouds to reach this level of precision. The objective is instead to obtain a stand-alone MW cloud mask and classification algorithm that can be used efficiently in forthcoming retrieval schemes of surface or atmospheric parameters from microwave satellite observations. This is an important tool over both ocean and land since the assimilation of the MW observations in the operational centers is independent from the other satellite observations.

Clear sky and low, medium, and opaque–high clouds can be retrieved over ocean and land at a confidence level of more than 80%. An information content analysis shows that AMSU-B provides significant information over both land and ocean, especially for the classification of medium and high clouds, whereas AMSU-A is more efficient over ocean when discriminating clear situations and low clouds.

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Catherine Prigent, Juan R. Pardo, and William B. Rossow

Abstract

Geostationary satellites provide revisiting times that are desirable for nowcasting and observations of severe weather. To overcome the problem of spatial resolution from a geostationary orbit, millimeter to submillimeter wave sounders have been suggested. This study compares the capabilities of various oxygen and water vapor millimeter and submillimeter bands for temperature and water vapor atmospheric profiling at nadir in cloudy situations. It shows the impact of different cloud types on the received signal for the different frequency bands. High frequencies are very sensitive to the cloud ice phase, with potential applications to cirrus characterization.

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Catherine Prigent, Frédéric Chevallier, Fatima Karbou, Peter Bauer, and Graeme Kelly

Abstract

This study describes the work performed at the European Centre for Medium-Range Weather Forecasts (ECMWF) to estimate the microwave land surface emissivities at Advanced Microwave Sounding Unit (AMSU)-A frequencies within the specific context and constraint of operational assimilation. The emissivities are directly calculated from the satellite observations in clear-sky conditions using the surface skin temperature derived from ECMWF and the Radiative Transfer for the Television and Infrared Observation Satellite Operational Vertical Sounder (RTTOVS) model, along with the forecast model variables to estimate the atmospheric contributions. The results are analyzed, with special emphasis on the evaluation of the frequency and angular dependencies of the emissivities with respect to the surface characteristics. Possible extrapolation of the Special Sensor Microwave Imager (SSM/I) emissivities to those of the AMSU is considered. Direct calculation results are also compared with emissivity model outputs.

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Filipe Aires, Fabrice Papa, Catherine Prigent, Jean-François Crétaux, and Muriel Berge-Nguyen

Abstract

The objective in this work is to develop downscaling methodologies to obtain a long time record of inundation extent at high spatial resolution based on the existing low spatial resolution results of the Global Inundation Extent from Multi-Satellites (GIEMS) dataset. In semiarid regions, high-spatial-resolution a priori information can be provided by visible and infrared observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). The study concentrates on the Inner Niger Delta where MODIS-derived inundation extent has been estimated at a 500-m resolution. The space–time variability is first analyzed using a principal component analysis (PCA). This is particularly effective to understand the inundation variability, interpolate in time, or fill in missing values. Two innovative methods are developed (linear regression and matrix inversion) both based on the PCA representation. These GIEMS downscaling techniques have been calibrated using the 500-m MODIS data. The downscaled fields show the expected space–time behaviors from MODIS. A 20-yr dataset of the inundation extent at 500 m is derived from this analysis for the Inner Niger Delta. The methods are very general and may be applied to many basins and to other variables than inundation, provided enough a priori high-spatial-resolution information is available. The derived high-spatial-resolution dataset will be used in the framework of the Surface Water Ocean Topography (SWOT) mission to develop and test the instrument simulator as well as to select the calibration validation sites (with high space–time inundation variability). In addition, once SWOT observations are available, the downscaled methodology will be calibrated on them in order to downscale the GIEMS datasets and to extend the SWOT benefits back in time to 1993.

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Satya Prakash, Hamid Norouzi, Marzi Azarderakhsh, Reginald Blake, Catherine Prigent, and Reza Khanbilvardi

Abstract

Accurate estimation of passive microwave land surface emissivity (LSE) is crucial for numerical weather prediction model data assimilation, for microwave retrievals of land precipitation and atmospheric profiles, and for a better understanding of land surface and subsurface characteristics. In this study, global instantaneous LSE is estimated for a 9-yr period from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and for a 5-yr period from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors. Estimates of LSE from both sensors were obtained by using an updated algorithm that minimizes the discrepancy between the differences in penetration depths from microwave and infrared remote sensing observations. Concurrent ancillary datasets such as skin temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) and profiles of air temperature and humidity from the Atmospheric Infrared Sounder are used. The latest collection 6 of MODIS skin temperature is used for the LSE estimation, and the differences between collections 6 and 5 are also comprehensively assessed. Analyses reveal that the differences between these two versions of infrared-based skin temperatures could lead to approximately a 0.015 difference in passive microwave LSE values, especially in arid regions. The comparison of global mean LSE features from the combined use of AMSR-E and AMSR2 with an independent product—Tool to Estimate Land Surface Emissivity from Microwave to Submillimeter Waves (TELSEM2)—shows spatial pattern correlations of order 0.92 at all frequencies. However, there are considerable differences in magnitude between these two LSE estimates, possibly because of differences in incidence angles, frequencies, observation times, and ancillary datasets.

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