A cloud physics investigation utilizing Skylab data

There are no author-identified significant results in this report

Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate

Atmospheric water-vapour profiling from passive microwave sounders over ocean and land. Part I: Methodology for the Megha-Tropiques mission

International audienceA water-vapour retrieval algorithm has been developed that uses satellite observations in the microwave region. It is based on neural-network modelling and includes a dedicated calibration scheme for the satellite observations. The water vapour is retrieved for clear and cloudy scenes, over both ocean and land surfaces. Precipitation cases are excluded. The atmospheric relative humidity profile is retrieved on six atmospheric layers, together with the total column water vapour. By-products are also retrieved by the algorithm, including surface temperature and microwave emissivities over the continents and surface wind speed over the ocean. A first version of a retrieval chain has been produced for the French-Indian Megha-Tropiques mission launched on 12 October 2011. The algorithm has been further developed for the instruments AMSR-E/HSB (resp. AMSU-A/MHS) on board the AQUA (resp. MetOp) platform, in order to test it on existing satellite observations. In this article, the principles of the inversion method are presented and the theoretical retrieval uncertainties are assessed using direct tests on simulated data as well as estimations using the traditional information-content analysis. Results of the retrieval algorithm will be evaluated in a companion article for AQUA and MetOp observations using comparisons with European Centre for Medium-Range Weather Forecasts (ECMWF) analysis and radiosonde measurements. © 2012 Royal Meteorological Society