2013 STAR Seminars

TBD

This seminar is crossposted from the ESSIC Seminar Series.

We present in this seminar the mathematical basis, the technical

We present in this seminar the mathematical basis, the technical implementation and the performances assessment of an iterative physical algorithm based on a Bayesian variational approach. This algorithm, called the Microwave Integrated Retrieval System (MiRS), is used operationally in NOAA to generate sounding, surface, hydrometeor and cryospheric parameters from a variety of microwave sensors including AMSU/MHS, SSMIS and ATMS onboard POES/Metop, DMSP and SNPP platforms, respectively. It is also applied routinely in a research mode (non-operationally) to data from AMSR-2, TMI and SAPHIR onboard GCOM-W, TRMM and Megha-Tropiques satellites, respectively.

The algorithm relies on the Community Radiative Transfer Model (CRTM), developed in the Joint Center for Satellite Data Assimilation (JCSDA), to simulate brightness temperatures and generate Jacobi with respect to all geophysical parameters. These two components, along with the background covariance matrix used, are critical for the physical inversion. In order to ensure a stable and fast processing, the inversion is undertaken after projecting it into a reduced space using the Empirically Orthogonal Functions (EOF). The state vector parameters are retrieved simultaneously, which ensures that the resulting geophysical solution fits the observations consistently, which is a necessary, although sometimes overlooked, condition for the inversion process.

Unstructured grids for ocean models have become increasingly popular in recent years. Los Alamos has developed an ocean model, MPAS-Ocean, that runs on an unstructured grid using the MPAS modeling framework. Here I describe progress in developing a companion sea-ice model, based on the Los Alamos sea-ice model CICE, that runs on the same unstructured grid. Rather than quadrilaterals, MPAS-CICE uses arbitrary sided polygons for its grid cells which allows variable resolution grids and the easy implementation of regional focused models. I will also describe a new prognostic salinity model recently implemented in CICE that models the various processes that move brine around the sea-ice. Such parametrizations are necessary for modelling sea-ice biogeochemistry.

During the afternoon of 29 June 2012, a complex of strong thunderstorms developed over Illinois and Indiana and then tracked southeastward over the Ohio Valley and central Appalachian Mountains by evening. As the convective storm complex moved over and east of the Appalachian Mountains at a forward speed of 45 to 50 knots, the leading storm line re-intensified and eventually produced widespread significant severe winds (> 65 knots) over northern Virginia and the Washington, DC metropolitan area, and finally over southern New Jersey as the mesoscale convective system (MCS) reached the Atlantic coast. This extraordinary derecho-producing convective system (DCS) event ultimately resulted in nearly a thousand severe wind reports from northern Illinois to the Atlantic Coast. This study will employ Geostationary Operational Environmental Satellites (GOES)-13 Rapid Scan Operations (RSO) water vapor (WV)-thermal infrared (IR) channel brightness temperature difference (BTD) imagery, level-II NEXRAD imagery, and Rapid Refresh (RAP) model-derived microburst prediction algorithm output, including the Microburst Windspeed Potential Index (MWPI) and vertical theta-e difference (Δθe), to demonstrate the development and evolution of severe DCS-generated winds. Severe downburst events from the time of initiation over northern Indiana to the time that the DCS moved over the Atlantic coast have been identified and documented. The comparison of NEXRAD imagery to Storm Prediction Center (SPC) high wind reports will emphasize the role of downburst clusters in the observation of regions of enhanced severe winds, especially over the Washington, DC-Baltimore, MD metropolitan areas. The combination of satellite, radar, and numerical model resources, visualized by McIDAS-V software, will describe the evolution of this DCS and will serve as an example of how to use this data in forecasting meso- to micro-scale severe wind events (i.e. downbursts, microbursts) embedded in larger-scale derechos.

NOAA Coral Reef Watch's (CRW) global near-real-time coral bleaching operational monitoring product suite is extensively used by US and international resource managers,reef scientists, and the general public to monitor thermal stress and predict the onset, development, and severity of mass coral bleaching. However, its algorithms are based solely on satellite sea surface temperature (SST) observations. The new experimental Light Stress Damage (LSD) introduced here is the first product to combine satellite-derived light and SST data to monitor/predict coral stress that can lead to bleaching.

The LSD product provides a relative measure of the effect of combined light and thermal stress on the coral photo-system. The LSD product is underpinned by a series of physiological experiments that allowed the formulation of the relationships between the excessive excitation energy (EEE), relative potential quantum yield (F_v/F_m), change in SST, and differences in total daily photosynthetically active radiation (PAR).

The LSD algorithm is then able to be formulated as a simple function of SST and PAR and is expressed as an index that mimics the reef-scale relative F_v/F_m.

The University of Queensland, National Oceanic and Atmospheric Administration, Australian Institute of Marine Science, and Great Barrier Reef Marine Park Authority have been awarded a major grant under the Australian Research Council's (ARC) Industry Linkage Grant Program to develop the LSD algorithm further.

The aim is to fully develop the science that underpins the algorithm, investigate aspects of mortality, expand the algorithm to include other environmental stresses, develop a field verification methodology, and investigate the future validity of the algorithm.