Submillimeter‐Wave Cloud Ice Radiometer: Simulations of retrieval algorithm performance

Abstract

[1] The Submillimeter-Wave Cloud Ice Radiometer (SWCIR), developed by the Jet Propulsion Laboratory to fly on the NASA DC-8, has ten channels from four receivers at 183, 325, 448, and 643 GHz. The SWCIR is designed to retrieve upper tropospheric cloud ice water path (IWP) and median mass equivalent sphere diameter (D me ). This paper describes the retrieval algorithm developed for the SWCIR, results from retrieval simulations, and the rationale for choosing the SWCIR frequencies. The retrieval algorithm uses Bayes theorem to combine prior information about atmospheric properties with radiative transfer simulations. The algorithm uses a precalculated database of stochastic cirrus/atmospheric profiles and corresponding simulated SWCIR brightness temperatures. Statistics of cirrus microphysics are derived from in situ cloud probe data obtained during midlatitude winter and tropical field experiments, and include correlations between temperature, particle size, and ice water content. The Bayesian algorithm effectively interpolates between database cases that approximately match the observed SWCIR brightness temperatures. Retrieval experiments are performed for midlatitude winter ice clouds with the SWCIR viewing downward from 12 km and for tropical anvil cirrus with the SWCIR viewing upward from 10 km. Each experiment simulates SWCIR brightness temperatures (with 1 K added noise) from 10000 random cloud/atmosphere profiles. For clouds with IWP greater than 5 g/m 2 (which contain 97% of the simulated total ice mass) the overall median retrieval error is about 30% for IWP and 15% for D me . The effects of liquid clouds just below ice clouds and of nonspherical ice particle shapes are small.

Performance simulations for a submillimetre-wave satellite instrument to measure cloud ice

“…Rodgers, 2000) and a Monte Carlo integration of the posterior distribution (e.g. Evans et al, 2002). In principle, these three methods can be set up to provide the mean state of the posterior distribution.…”

“…The CEPEX microphysics are based on the 2DC probe (Evans et al, 2002), while the CRYSTAL-FACE microphysics are based on FSSP, 2DC, and HVPS probes (Evans et al, 2005). The midlatitude winter statistics are based on 2DC and 2DP probes from three flights during the ARM 2000 Cloud Intensive Operation Period (IOP) (Heysmfield et al, 2002).…”

“…Evans et al, 2002Evans et al, , 2005 and first cloud ice retrievals with the existing satellite limbsounding submillimetre sensors have been published Eriksson et al, 2007). However, aircraft instruments are useful for specific campaigns but cannot provide global coverage, while the limb-sounding geometry results in poor horizontal resolution.…”

Ice hydrometeor profile retrieval algorithm for high-frequency microwave radiometers: application to the CoSSIR instrument during TC4

“…The more general Bayesian formulation has been used in some high-frequency microwave ice cloud retrieval algorithms, usually with a Monte Carlo integration approach (Evans et al, 2002(Evans et al, , 2005Seo and Liu, 2005;Rydberg et al, 2009). The Monte Carlo integration (MCI) method randomly generates atmosphere/cloud cases according to a prior probability density function and simulates instrument measurements for each case with a radiative transfer model to create a "retrieval database" of simulated observations and corresponding retrieval quantities.…”

A Bayesian algorithm for the retrieval of liquid water cloud properties from microwave radiometer and millimeter radar data

“…Following Evans et al [2002], we define a normalized error, d q = (q ret À q true )/s q , where q ret and q true are the retrieved and true values of a parameter q, and s q is the retrieved standard deviation. Table 3 lists the fraction of cases for which the normalized error is within ±1 and ±3 for each of the retrieved variables in the two cases.…”