Past 2009 JCSDA Seminars

OMI is a Dutch-Finnish built instrument that was launched on the Aura satellite in July 2004. Its original purpose was to extend the long-term record of ozone created by TOMS and SBUV- series of instruments - a record that goes back to 1970. However, owing to its hyperspectral capability, high spatial resolution, and daily global coverage, OMI is producing many more products related to atmospheric chemistry and air quality with better accuracy and precision than its predecessor instruments. Despite its spectacular success, use of OMI data for scientific studies remains a challenge. Like most nadir-viewing passive remote sensing instruments OMI algorithms depend on a priori information for accurate retrieval. However, for most OMI products, with the exception of ozone, the quality of available a priori data is quite limited. In principle, this limitation can be overcome by assimilating OMI data with modern high-resolution chemical- transport models. However, so far there has been limited success in assimilating non-meteorological data into data assimilation systems. This is perhaps because the fundamental nature of the two problems is quite different. Progress in this area will require close coordination between the measurement and data assimilation community.

An observation-space global 4D-Var atmospheric data assimilation system, NAVDAS-AR, for the U.S. Navy has been successfully implemented and tested at Fleet Numerical Meteorology and Oceanography Center (FNMOC). NAVDAS-AR will replace NAVDAS, the 3D-Var observation-space data assimilation system, to provide the analysis for the Navy Operational Global Atmospheric Prediction System (NOGAPS) in the near future. In this talk, we will give a brief background of the development and testing of the NAVDAS-AR. We will present the weak constraint variational formulism and the minimization algorithm used in the system. Some of the results obtained from a recent validation test report required for the NAVDAS-AR operational transition will be shown. We will also give a brief description of some of the NAVDAS-AR capabilities that were not included in the current operational implementation. Planned upgrades to the operational 4D-Var system will also be discussed. (L. Xu, N. Baker, B. Ruston, T. Hogan, P. Pauley, and S. Swadley, NRL, Monterey, CA; T. Rosmond and B. Chua, SAIC, Monterey, CA; R. Pauley, FNMOC, Monterey, CA)

This talk will present summarize research by our group at the University of Arizona related to GPS radio occultation (RO) and a next generation RO system called the Active Temperature, Ozone and Moisture Microwave Spectrometer (ATOMMS). GPS RO is receiving more attention with time as the weather and climate communities become aware of its features such as ~200 m vertical resolution, high precision, self calibration and high accuracy and retrievals in both clear and cloudy conditions. Under the assumption of spherical symmetry, the refractivity profiles derived from bending angle profiles are unique (except sometimes in the low latitude boundary layer). The instruments are small and inexpensive such that a constellation of these receivers like the 6 satellite COSMIC mission can provide full diurnal coverage. These features are well suited for weather prediction and climate.

We will summarize our results studying the low latitude water cycle using the wealth of information from the CHAMP and COSMIC GPSRO missions about vertical water distribution between 2.5 and 8.5 km. We have developed a new method to grid the GPS RO data, identified a preliminary free tropospheric water vapor-based ENSO index and found new predictive skill for ENSO. We have also uncovered indications of a substantial negative feedback between the 2007 El Nino and 2008 La Nina that may be related to why 2008 was a relatively cold year.

We have been working to increase the NWP impact of GPSRO data in the lower troposphere by improving the error covariance and correcting the cause of a negative refractivity bias in the lower troposphere due to a combination of receiver signal tracking problems (which improved greatly with the open loop receivers on COSMIC) and super-refraction, a ducting effect that often occurs at the top of the marine boundary layer that has limited the use of GPSRO data in the lower troposphere. We are working to implement an algorithm we developed that accounts and corrects for super-refraction.

While quite powerful, GPSRO is limited by GPS frequencies chosen to minimize interaction with the atmosphere. We will present an overview of a new RO system that we are developing at the University of Arizona for climate that probes the atmosphere at frequencies near absorption lines of key atmospheric species. ATOMMS combines many of the best features of GPSRO and the Microwave Limb Sounder (MLS). An ATOMMS instrument prototype is near completion in preparation for an aircraft-to-aircraft occultation demonstration in 2010.

The measurement of global wind profiles is widely recognized as the most important unmet observational requirement for improving numerical weather forecasts. The wind field has a unique dynamical role in forcing the mass field to adjust to it at small scales in the extratropics and at all scales in the tropics. Inferring the wind field through the measurement of other quantities, as is currently done, leaves much room for improvement in the analyses for numerical forecasts and for climate monitoring. Doppler lidar technology can provide the direct measurement of wind profiles from space, with the first space- based demonstration, the European Space Agency’s Atmospheric Dynamics Mission (ADM), scheduled for launch in Spring 2011. ADM will measure line-of-sight winds via a single perspective view of the target atmospheric volume. In the U.S., a wind lidar concept has been developed which will measure the horizontal vector wind for the first time from two perspectives of the target volume. The U.S. concept also combines two different technologies, referred to as the "hybrid" approach, to obtain wind profiles from near the surface to the lower stratosphere.

The U.S. wind lidar space-based concept will be discussed as well as some recent forecast impact results obtained with wind lidar data collected by aircraft during the THORPEX Pacific Area Regional Campaign (T-PARC) in Fall 2008.

Accurate high resolution specification of sea surface temperature (SST) is important for regional weather forecasting studies and coastal ocean applications. Chelton et al. (2007) and Lacasse et al. (2008) showed that the use of coarse resolution SST products such as from the real-time global (RTG) SST analysis (Thiebaux et al. 2003) in regional weather forecast models do not properly portray the fluxes of heat and moisture from the ocean that drive the formation of low level clouds and precipitation. High resolution SSTs may also be important for hurricane track and intensity forecasts and useful to verification of ocean circulation models. A polar orbiting data compositing technique, which provides spatially continuous, accurate, high-resolution SST fields using data from the Moderate-resolution Imaging Spectrometer (MODIS) on NASA's Terra and Aqua satellites, was developed by Haines et al. (2007). Case et al. (2008) presented a detailed analysis of the impact of the composite SST product in coastal regions. However, the approach was limited during periods of long-term cloud cover where latency of past data reduced the accuracy of the data presented in the composites. Recently, an enhanced compositing technique was developed to circumvent shortcomings of the Haines et al. (2007) approach by including AMSR-E SST data in the compositing process. The enhanced scheme also incorporates a more sophisticated temporal weighting scheme which considers bias, observational errors and spatial resolution along with the latency of the SST data in the generation of the high resolution composites. The enhanced SST composite product is produced four times a day in near real-time over the ocean regions surrounding the continental U.S. The product is being integrated into NASA's Short Term Prediction and Research Transition (SPoRT) project (Jedlovec et al. 2006) and distributed to the NWS, other government agencies, and the public for use in regional weather forecast applications. Prospective users can also get this product from the Physical Oceanography DAAC in standard L3P format later this year. The presentation will describe this work and present examples of the impact of the product on short-term weather forecasts.

NOAA's role in energy is multi-faceted. To plan the energy systems of the future, the industry needs NOAA to provide information about the potential environmental impacts of these systems, and the pertinent observations and weather forecasts that are necessary before renewable energy (RE) can be integrated into the grid in large amounts. Further, current numerical weather prediction models have not been optimized to address the needs of the RE industry. In addition, increased understanding of the complex relationship between climate and renewable energy resources is required to support efficient and intelligent development of a carbon-free energy system. This seminar will present the needs of the RE industry that NOAA could address, as well as plans for the One-NOAA Energy Initiative for FY2012-2016.

One of the most challenging weather forecast problems in the Southeastern U.S. is daily summertime pulse-type convection. During the summer, atmospheric forcing is usually weak in this region; thus, convection typically initiates in response to local forcing along sea/lake breezes, and other discontinuities often related to horizontal gradients in surface heating rates. For this study, it is hypothesized that high-resolution, consistent representations of surface properties such as soil moisture and sea surface temperature (SST) are necessary to better simulate the interactions between the surface and atmosphere, and ultimately improve predictions of local circulations and summertime pulse convection.

This evaluation focuses on a case study period from June-August 2008 using the Advanced Research dynamical core of the Weather Research and Forecasting (WRF) model. The primary goal is to improve simulations of pulse-type convection using the NASA Land Information System (LIS) and SPoRT's high-resolution Moderate Resolution Imaging Spectroradiometer sea surface temperature composites to initialize the land and sea-surface variables, respectively. The Developmental Testbed Center's Meteorological Evaluation Tools (MET) package is employed to produce verification statistics, including neighborhood precipitation verification and output from the Method for Object-Based Diagnostic Evaluation tool. The WRF model configuration, LIS spin-up run, and MET verification results will be presented in this seminar.

The Arctic area has undergone a significant surface warming over the last 30-40 years and simultaneously the sea ice cover has decreased significantly. The Arctic warming is about twice as large as the average global surface warming for the same time period. It is commonly conjectured that the retreat of the summer Arctic sea ice cover and the positive ice-albedo feedback is the main reason for the enhanced Arctic warming. We have analyzed the vertical structure of the Arctic warming over the past 30 years using re-analysis data. We find that the warming maximum is not at the surface but rather at about 3 km height. This leads us to look for other possible physical mechanisms responsible for the warming. We find that the warming maximum is linked to an increased baroclinic heat transport into the Arctic region. How this increased heat transport may be coupled to global warming remains an open question. We also discuss limitations of using re- analysis data to determine climate trends.

In the current versions of both variational and ensemble data assimilation a very important assumption is made about how the errors are distributed. This assumption is that the errors are Gaussian (normally) distributed. However, this assumption is using the implicit property of the Gaussian distribution that the difference between two Gaussian random variables is also a Gaussian random variable. Therefore, this is implying that the state variables and the observations are also Gaussian distributed. This is not possible for the positive definite variables which can not go negative. There are some techniques to deal with variables which are lognormally distributed through using another property of the Gaussian distribution rather than assuming a Gaussian fit. This property, or rather its inverse, is that the logarithm of a lognormal random variable is a Gaussian distributed random variable. This approach introduces a bias into the analysis solution as we will demonstrate. In this paper we shall present the outline of the derivations for non-Gaussian data assimilation with respect to lognormal random variables. We shall present a 3D and 4D variational approach, and demonstrate these techniques with the Lorenz'63 model, which can assimilate Gaussian and lognormal random variables, both background errors and observations errors, simultaneously.

NASA's Aquarius Mission is now planned to launch in mid-2010 to begin a 3 year (baseline) mission to measure sea surface salinity (SSS) monthly, over the open ocean, with an accuracy of 0.2 on the practical salinity scale (pss), and 150 km spatial resolution. It is the primary component of the international partnership satellite Aquarius/SAC-D, including Argentina, Italy, Canada, France and Brazil. The satellite will be placed in a sun- synchronous polar orbit that repeats every seven days, and will carry several complimentary scientific instruments. The primary sensor is an L-band microwave radiometer/radar system to measure the surface microwave brightness to retrieve SSS and the radar backscatter to correct for surface wind and sea state. This presentation will review the science background, SSS remote sensing and how it works, the Aquarius/SAC-D Mission design, calibration and data validation, algorithms and simulators, ground system, science teams and data access to NOAA and the broader science community.

High spatial and temporal resolution global precipitation estimates are important for understanding the Earth's energy and water cycles. Thus, the upcoming NASA/JAXA Global Precipitation Measurement (GPM) mission seeks to estimate precipitation (falling snow as well as liquid rain) globally using physically-based retrieval approaches. The GPM concept centers on deploying a Core spacecraft carrying a dual-frequency precipitation radar and a microwave radiometric imager with channels from 10 to 183 GHz to serve as a precipitation physics observatory and a calibration reference to unify a constellation of dedicated and operational passive microwave sensors. A summary of the GPM mission, scientific objectives, and sensors will be provided. Next, progress and challenges associated with early development work for GPM snowfall detection and estimation will be presented. The focus is on NOAA's AMSU-B (MHS) radiometer data and field campaign data collected during the Canadian CloudSat/CALIPSO Validation Project (C3VP) from Oct 2006 to March 2007. Approaches for detecting falling snow and obtaining surface emissivity will be reviewed. This seminar will show that surface emission contributions to the satellite observed brightness temperatures over land can add uncertainty in detecting and estimating falling snow. It will also discuss mitigation approaches for reducing these uncertainties. The above work and future work to incorporate knowledge about falling snow retrievals into the framework of the expected GPM Bayesian retrievals will be described during this presentation.

The hyperspectral nature of AIRS provides high-quality soundings that, along with their asynoptic observation time over North America, are attractive sources to fill the spatial and temporal data voids in upper air temperature and moisture measurements for use in data assimilation and numerical weather prediction. Observations from AIRS can be assimilated either as direct radiances or retrieved thermodynamic profiles, and the Short-Term Prediction Research and Transition (SPoRT) Center at NASA's Marshall Space Flight Center has used both data types to improve short-term (0-48h), regional forecasts. Working with both types of data has its challenges and limitations. This presentation is aimed at sharing SPoRT's experiences using AIRS radiances and retrieved profiles in regional data assimilation activities by showing that proper handling of issues—including cloud contamination and land emissivity characterization—are necessary to produce optimal analyses and forecasts. Additionally, results of these data assimilation activities and future work will be shared.