Peter M. Norris

Assimilation of Satellite Cloud Data into the GMAO Finite-Volume Data Assimilation System Using a Parameter Estimation Method. Part I: Motivation and Algorithm Description

Abstract General circulation models are unable to resolve subgrid-scale moisture variability and associated cloudiness and so must parameterize grid-scale cloud properties. This typically involves various empirical assumptions and a failure to capture the full range (synoptic, geographic, diurnal) of the subgrid-scale variability. A variational parameter estimation technique is employed to adjust empirical model cloud parameters in both space and time, in order to better represent assimilated International Satellite Cloud Climatology Project (ISCCP) cloud fraction and optical depth and Special Sensor Microwave Imager (SSM/I) liquid water path. The value of these adjustments is verified by much improved cloud radiative forcing and persistent improvement in cloud fraction forecasts.

Monte Carlo Bayesian inference on a statistical model of sub‐gridcolumn moisture variability using high‐resolution cloud observations. Part 2: Sensitivity tests and results

Part 1 of this series presented a Monte Carlo Bayesian method for constraining a complex statistical model of global circulation model (GCM) sub-gridcolumn moisture variability using high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) cloud data, thereby permitting parameter estimation and cloud data assimilation for large-scale models. This article performs some basic testing of this new approach, verifying that it does indeed reduce mean and standard deviation biases significantly with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud-top pressure and that it also improves the simulated rotational-Raman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the Ozone Monitoring Instrument (OMI). Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows non-gradient-based jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast, where the background state has a clear swath. This article also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in passive-radiometer-retrieved cloud observables on cloud vertical structure, beyond cloud-top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification from Riishojgaard provides some help in this respect, by better honouring inversion structures in the background state.

Monte Carlo Bayesian inference on a statistical model of sub‐gridcolumn moisture variability using high‐resolution cloud observations. Part 1: Method

A method is presented to constrain a statistical model of sub-gridcolumn moisture variability using high-resolution satellite cloud data. The method can be used for large-scale model parameter estimation or cloud data assimilation. The gridcolumn model includes assumed probability density function (PDF) intra-layer horizontal variability and a copula-based inter-layer correlation model. The observables used in the current study are Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-top pressure, brightness temperature and cloud optical thickness, but the method should be extensible to direct cloudy radiance assimilation for a small number of channels. The algorithm is a form of Bayesian inference with a Markov chain Monte Carlo (MCMC) approach to characterizing the posterior distribution. This approach is especially useful in cases where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach is not gradient-based and allows jumps into regions of non-zero cloud probability. The current study uses a skewed-triangle distribution for layer moisture. The article also includes a discussion of the Metropolis and multiple-try Metropolis versions of MCMC.

Cloud field assimilation using simulated Triana satellite data

Abstract In the next few years, NASA plans to launch satellite Triana, a deep space Earth observatory that will take a full-disk view of the sunlit side of the Earth. Triana carries two instruments, EPIC, which will deliver Science products such as total precipitable water, cloud height, aerosol index, total ozone, and a global visible cloud field image, and NISTAR, which obtains precise radiometry integrated over the entire sunlit disk. Using a contemporary atmospheric model (namely the Eta model), we have started a project whose goal is to simulate some of the Triana observations and to assess the impact of Triana data for weather and climate predictions. In this paper, we report on the results of numerical experiments assimilating temperature profiles with and without cloud liquid water for inferring tropical and extra-tropical atmospheric states. We also assess the impact of initializing cloud liquid water for short term forecasts and data assimilation cycles.

Implementation of an eta belt domain on parallel systems

We extend the Eta weather model from a regional domain into a belt domain that does not require meridional boundary conditions. We describe how the extension is achieved and the parallel implementation of the code on the Cray T3E and the SGI Origin 2000. We validate the forecast results on the two platforms and examine how the removal of the meridional boundary conditions affects these forecasts. In addition, using several domains of different sizes and resolutions, we present the scaling performance of the code on both systems.