Search Results

You are looking at 1 - 5 of 5 items for :

  • Author or Editor: Catherine Prigent x
  • Journal of Applied Meteorology and Climatology x
  • All content x
Clear All Modify Search
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.

Full access
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.

Full access
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.

Full access
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.

Full access
Jean-Pierre Chaboureau, Nathalie Söhne, Jean-Pierre Pinty, Ingo Meirold-Mautner, Eric Defer, Catherine Prigent, Juan R. Pardo, Mario Mech, and Susanne Crewell

Abstract

The simulations of five midlatitude precipitating events by the nonhydrostatic mesoscale model Méso-NH are analyzed. These cases cover contrasted precipitation situations from 30° to 60°N, which are typical of midlatitudes. They include a frontal case with light precipitation over the Rhine River area (10 February 2000), a long-lasting precipitation event at Hoek van Holland, Netherlands (19 September 2001), a moderate rain case over the Elbe (12 August 2002), an intense rain case over Algiers (10 November 2001), and the “millennium storm” in the United Kingdom (30 October 2000). The physically consistent hydrometeor and thermodynamic outputs are used to generate a database for cloud and precipitation retrievals. The hydrometeor vertical profiles that were generated vary mostly with the 0°C isotherm, located between 1 and 3 km in height depending on the case. The characteristics of this midlatitude database are complementary to the GPROF database, which mostly concentrates on tropical situations. The realism of the simulations is evaluated against satellite observations by comparing synthetic brightness temperatures (BTs) with Advanced Microwave Sounding Unit (AMSU), Special Sensor Microwave Imager (SSM/I), and Meteosat observations. The good reproduction of the BT distributions by the model is exploited by calculating categorical scores for verification purposes. The comparison with 3-hourly Meteosat observations demonstrates the ability of the model to forecast the time evolution of the cloud cover, the latter being better predicted for the stratiform cases than for others. The comparison with AMSU-B measurements shows the skill of the model to predict rainfall at the correct location.

Full access