2008 STAR Seminars

The USA National Phenology Network (USA-NPN) is an emerging and exciting partnership between federal agencies, the academic community, and the general public to monitor and understand the influence of seasonal cycles on the Nation's resources. The goal of the USA-NPN (www.usanpn.org) is to establish a wall-to-wall science and monitoring initiative focused on phenology, the seasonal pulse of the biosphere and thus the gateway to climatic effects on ecosystems and ecosystem services. Periodic plant and animal cycles driven by seasonal variations in climate are the most fundamental biotic oscillations connected to human activities. They set the stage for dynamics of ecosystem processes, determine land surface properties, control biosphere- atmosphere interactions, and affect food production, health, conservation, and recreation. Phenological data and models at local to national scales have applications related to scientific research, education and outreach, as well as to stakeholders interested in agriculture, tourism and recreation, human health, and natural resource conservation and management. However, the predictive potential of phenology requires a new data resource--a national network of integrated phenological observations and the tools to access and analyze them at multiple scales. The USA-NPN will (1) integrate with other formal and informal science observation networks (e.g., NEON, LTER, Ameriflux, NPS I & M, OBFS, public gardens, conservation groups) including regional phenology networks; (2) utilize and enhance remote sensing products, emerging technologies and data management capabilities; and (3) capitalize on myriad educational opportunities and a new readiness of the public to participate in investigations of nature on a national scale. This talk will illustrate how phenology is an emerging integrative science for assessing impacts of climate change and for increasing citizen awareness and participation in understanding environmental impacts of human activities on Earth systems.

Determination of Aerosol Optical Depth (AOD) by satellite remote sensing measurements over land is complicated by the fact that the Top of Atmosphere (TOA) reflectance is a combination of the desired atmospheric path reflectance as well as the ground reflectance. Unfortunately, inaccurate surface modeling results in inaccurate AOD retrieval as well as reducing spatial resolution. In this presentation, we primarily focus on the use of simultaneous MODIS and AERONET sky radiometer data to refine the surface albedo models regionally and improve on the current AOD operational retrieval. In particular, we show that the correlation coefficient assumption used in the MODIS Collection (5) model between the VIS and MIR channels used for surface reflection parameterization in urban areas such as New York and Mexico City is severely underestimated. This is demonstrated both directly using high spatial imagery data from Hyperion and indirectly by constraining MODIS TOA reflection data with Aeronet Sky radiometer AOD retrievals. In particular, we find that combining the satellite and radiometer measurements allows us to generate a regional VIS/MIR surface reflectance correlation coefficient map at spatial resolutions up to 1.5km. Application of the regional VIS/MIR surface reflectance ratio model is shown to completely remove the bias and reduce uncertainty at the operational resolution of 10km as well as at higher resolutions to 1.5km resolution. Furthermore, the regional surface albedo model results in reduction of artificial AOD hotspots which often are seen in the operational retrieval.

In exploring angular albedo effects, we first verify that the correlation coefficients are insensitive to scattering angle as expected. On the other hand, the individual channel reflectences show clear angular dependences which we fit to the operational Kernal Model. However, we find that errors resulting from lambertian assumption are shown to within the errors that can be associated with albedo variability. Conversely, we also explore the MISR retrieved AOD product with AERONET derived AOD over urban areas and show that due to an overestimate of the surface by MISR, the AOD retrieval is underestimated. Finally, we apply the modified surface models data to GOES satellite observations and show that AOD retrieval from GOES using the modified regional model is in better agreement to Aeronet.

Global ocean and land precipitation variations are an important part of the climate system. For the satellite period, since 1979, near-global observations are available and analyses have been developed. Prior to then there are almost no observations of oceanic precipitation. This seminar presents results of some recent reconstructions of historical near-global precipitation. We developed two different reconstructions for analyzing oceanic variations. One is based on fitting the available historical gauge data to a set of large-scale spatial modes. It resolves much of the interannual variance but has problems with multi-decadal variations. The other uses covariance between combined fields of SST and SLP and precipitation, and appears capable of resolving multi-decadal variations. In the future we plan to combine these two methods.

The CSU RAMS forecast model (with nested grids) was used to simulate observed mesoscale weather with horizontal grid spacing as small as 400 m. For fire simulations, equally-distributed artificial fire hotspots were added to a simulated mesoscale event. In addition, location and fire temperature information for real fires (from CIMSS ABBA-retrieved datasets, based on GOES- 11/12) were inserted into various simulated mesoscale events, both with and without clouds.

The RAMS output is used as input to an observational operator, which in conjunction with OPTRAN code and radiative transfer models, to produce synthetic radiances for three GOES-R Advanced Baseline Imager (ABI) wavelengths (3.9 µm, 10.35 µm, 11.2 µm). From those radiances, GOES-R ABI synthetic imagery is produced at the appropriate footprint by using an approximation for the ABI point spread function (PSF). Finally, McIDAS files and GIF imagery and animations were created for all datasets, for use as Algorithm Working Group (AWG) proxy datasets for algorithm testing and verification.

Four fire cases will be presented: Case 1: Artificial fire hot spots embedded in Kansas severe weather event - 8 May 2003; Case 2: Agricultural fires in Central America - 24 April 2004; Case 3: Lightning induced fires in Southern California, for both 23 and 26 October 2007; and an unusual fire flare-up Case Study: the Rich Wildland Lightning Fire, Northern California - 30 July 2008.

Over the tropical latitudes, there are abundant clouds overshooting the Tropical Tropopause Layer (TTL). These deep convective clouds (DCCs) were used for testing if solar channel calibration is possible, using collocated IR window channel measurements. The proposed method was tested first using MODIS measurements. DCCs were determined from MODIS 10.8 µm brightness temperature (TB) measurements by applying the criteria of TB < 190 K, and then MODIS-derived cloud optical thickness (t) and effective radius (Re) of determined DCCs were examined to find typical values representing DCCs. It was found that most of t of those selected DCCs are greater than 100 or appear to be larger than 200. In addition, re-distributions show a sharp peak centered at around 22 µm. MODIS visible channel radiances were then simulated using a modified SBDART radiative transfer model with Baum et al. scattering data base for homogeneous overcast ice clouds of t = 200 and Re = 22 µm, based on the assumption that reflected visible radiances are in near saturation when t > 200. The comparison of simulated radiances with MODIS-observed radiances for one year of 2006 demonstrates that visible channel measurements can be calibrated within a ±5% uncertainty range on a daily basis. Furthermore, considering that DCCs are abundant over the tropical latitudes and that the algorithm only requires DCC determination, the method can be easily adopted for the calibration of visible sensors aboard both geostationary and low- orbiting satellites.

User requirements for high-resolution sea surface temperature (SST) data have increased substantially in recent years. In response to this need, NESDIS/STAR has developed a daily 0.1° x 0.1° resolution SST analysis that combines SST retrievals obtained from the imagers carried on NOAA's operational POES and GOES platforms. This analysis, which became an operational product on June 17, 2008, is also intended as an eventual replacement for the AVHRR- only 100-km and 50-km global analyses, and a 14-km regional analysis, that have been produced on an operational basis by OSDPD for many years. Inclusion of geostationary data brings a substantial increase in coverage and volume, along with some significant challenges. The two major issues have been the need for an agile bias correction of GOES SST retrievals, and the need to balance the twin requirements of detail preservation and low noise. This seminar will describe the underlying analysis methodology and the techniques employed to combat the afore- mentioned issues. Comparisons with other high-resolution analyses will be also shown, along with some validation against in situ data. The prospects for inclusion of other datasets, particularly from microwave sensors and other geostationary platforms will also be discussed.

For three years, NOAA Coral Reef Watch has maintained a base of operations in Townsville, Australia, that has provided an opportunity for closer connections between the world's two largest coral reef research efforts. This talk will outline the various agencies and institutions, their capabilities/interests, and their projects in collaboration with CRW. These include satellite algorithm development for the monitoring of coral bleaching, coral disease outbreak, ocean acification and algal blooms. These collaborative efforts have provided CRW with leveraged research, funded through Australia government and industry, worth several million dollars. Overlapping research desires and, often times, complementary approaches and abilities have produced significant advancements.

NOAA has identified a significant demand for a concentrated research effort in support of an overarching objective of the Climate Research and Modeling Program of the NOAA Climate Goal, specifically," ... to develop and improve the capability to make intra-seasonal, seasonal, and decadal-scale predictions of climate and projections of future climate change on global to regional scales." Improvements in the predictive capability on weekly, monthly, seasonal and decadal time scales can be greatly accelerated by leveraging the expertise within the external research community. To harvest this expertise, NOAA has formed the Climate Test Bed (CTB) to accelerate the transfer of research and development into improved NOAA operational climate forecasts, products, and applications.

This presentation will describe CTB activities and plans as they support the paradigms of research-to-operations (R2O) and operations-to-research (O2R).

More than 400 tropical cyclones that occurred worldwide from 1999 through 2005 were examined. Changes in tropical cyclone behavior were observed using geostationary satellite imagery and archival data from the major tropical centers. The upper air environment was observed on satellite data, with emphasis on 6.7 micrometer water vapor imagery, and forecast model winds and temperatures between 500mb and 300mb. Seven categories of tropical cyclone behavior, such as turns and intensity changes were defined; and, 361 "events" were identified and analyzed. Likewise, 6 categories of changes in the upper air environment were defined; and, 376 "events" were identified from the tropical cyclone cases.

Specific types of changes in the upper air environment were found to be related to certain changes in tropical cyclone behavior. Two specific types of tropical cyclone cloud patterns were observed with weakening storms. Middle tropospheric dry air that arrived at the cold cloud shield boundaries of tropical cyclones at small angles and was ingested into the storms, was correlated with spiral shaped "intrusions" in the storm cloud pattern and weakening. Eye replacement cycles were also likely with this type of environmental change. Opening of adjacent upper air systems, that brought flow to the tropical cyclone cold cloud shields at large angles was correlated with cloud pattern deforming and weakening. Four types of environmental changes were well correlated with storm formations and intensification. Although the 153 right turns and 79 left turns were well related to specific categories of upper air changes, the relationships did not provide the quantitative information necessary for accurate track forecasting. However, specific categories of environmental changes related to turns were highly correlated with storm intensity changes during or after the turns. Relationships found in the study are likely to be useful in choosing model results, when various model forecasts diverge. Overall, changes in the adjacent upper air ridge and anticyclones made the greatest contributions to changes in tropical cyclone behavior. The eastward passages of short wave ridges in the westerlies, on the poleward side of storms, was found to be a particularly important type of environmental change affecting tropical cyclone behavior.

Accurate estimation of spatially distributed CO₂2 fluxes is of great importance for regional and global studies of carbon balance. We have found that in irrigated and rainfed crops (maize and soybean) as well as in grasslands, carbon dioxide exchange is closely related to total crop and grass chlorophyll content. The finding allowed development of a new technique for remote estimation of chlorophyll specifically for assessing carbon dioxide exchange / gross primary production (GPP). The technique is based on reflectance in two spectral channels: the near- infrared and either the green or the red-edge. The technique provided accurate estimations of daily carbon dioxide exchange. Validation using independent datasets for irrigated and rainfed maize and soybean documented the robustness of the technique. We report also about applying the developed technique for GPP retrieval from data acquired by both an airborne hyperspectral imaging spectrometer (AISA-Eagle) and ETM+ Landsat. The Chlorophyll Index, retrieved from Landsat ETM+ data, was found to be an accurate surrogate measure for daily carbon dioxide exchange with a root mean square error of GPP prediction of less than 1.58 g C m^-2d^-1 in a GPP range of 1.88 g C m^-2d^-1 to 23.1 g C m^-2d^-1. These results suggest new possibilities for analyzing the spatio- temporal variation of the GPP of crops using not only the extensive archive of Landsat Thematic Mapper imagery acquired since the early 1980s but also the 500-m/pixel data currently being acquired by MODIS.

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995- 2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data are being provided in an experimental mode to selected National Weather Service (NWS) forecast offices in Southern and Eastern Region. This effort is designed to help improve our understanding of the application of these data in operational settings.

Coral reefs live within a fairly narrow envelope of environmental conditions constrained by water temperatures, light, salinity, nutrients, bathymetry and the aragonite saturation state of seawater. As documented in numerous studies, the world's coral reefs are "in crisis" as a result of human impacts on their environment. While local stresses currently dominate, coral reefs are increasingly confronted with global-scale changes due to rising greenhouse gas concentrations. These changes are rapidly modifying the environmental envelope of coral reefs through both increased thermal stress and ocean acidification. In the former case, there is a well-documented relationship between thermal stress and the response of corals that include coral bleaching, disease, and mortality. Clear tolerance thresholds exist beyond which high temperature and accumulated thermal stress have deleterious effects. However, the synergistic effects of increasing temperature and ocean acidification are not yet fully understood. At this time, there is mounting concern that decreasing pH and aragonite saturation state will cause net reef accretion to cease or become negative. The threshold at which this could occur is likely to be reached much sooner than the pH drop necessary to induce carbonate dissolution. Both the thermal and chemical limits that control coral survival and reef growth will likely be passed before 2100 assuming even conservative projections reported in the 4th Assessment Report of the Intergovernmental Panel on Climate Change. This talk, based in part on the review paper highlighted with the cover of Science on 14 December, will discuss these thresholds and their ramifications for ecosystems and resource management.

A strong and positive coupling between sea surface temperature (SST) and surface wind speed on scales shorter than about 1000 km is well established from satellite measurements of surface winds by the QuikSCAT scatterometer and SST by the Advanced Microwave Scanning Radiometer (AMSR). This ocean-atmosphere interaction is clearly evident in the ECMWF global forecast model, although it is underestimated by about a factor of two. The SST influence on surface winds is barely detectable in the NCEP global forecast model. Simulations with the Weather Research & Forecasting (WRF) mesoscale model suggest that this is due to a combination of inadequate resolution of the SST boundary condition used for the NCEP model and underestimation of vertical mixing in the marine atmospheric boundary layer.

CIOSS research is "on the edge" in a number of ways. First, by definition, all research occurs on the edge of knowledge. Next, considering spatial dimensions, remote sensing of the ocean occurs at the very top edge of the ocean, due to the strong absorption of electromagnetic radiation (EMR) by water. This is a major difference between oceanographic and atmospheric remote sensing. At CIOSS, we also have a focus on the horizontal edge of the ocean - the coastal environment. Several efforts are underway to push microwave (active and passive) remote sensing closer to the coast, where contamination of EMR signals is caused by reflection and emision from the land into the antenna side-lobes. Finally, some of our ocean color group work is with hyperspectral data, pushing at the edges of spectral and spatial resolution. Examples will be presented of ongoing research at CIOSS in all of these, with a special emphasis on retrieving altimeter data closer to the coast.

Warming climates have been widely recognized to advance spring vegetation phenology. However, the delayed responses of vegetation phenology to rising temperature and their mechanisms are poorly understood. To investigate seasonal variations in AVHRR NDVI from 1982 to 2005, we developed a sigmoidal model (describing vegetation seasonal growth) to fit the NDVI temporal trajectory which was used to identify vegetation phenology. Integrating AVHRR-based phenology and climate data during last 25 years, we revealed the mechanisms of diverse responses of vegetation phenology to climate changes in North America. From 40°N northwards, the decrease in chilling days by winter warming temperature has little impacts on spring thermal-time requirement for vegetation greenup onset. Thus, spring warming temperature has constantly advanced greenup onset by 0.32 days/year. However, from 40°N southward, the shortened winter chilling days are insufficient for fulfilling plant chilling requirement, so that the thermal-time requirement for greenup onset during spring increases gradually. Consequently, vegetation greenup onset changes progressively from an early trend to a later trend along the latitude transition zone from 40-31°N. The greenup onset is delayed by 0.15 days/year below 31°N. Finally, by combining phenology models, we found that warming climates trigger the poleward shift of phenological transition zone with a rate of 0.1 latitude degree per year.

Over 20 STAR scientists will be presenting oral and poster presentations at the 88th Annual AMS Meeting in New Orleans, LA during the week of January 20, 2008. Come see overviews of many of these talks at the various symposia in New Orleans next week.

The attitude (or orientation) of a Geostationary Operational Environmental Satellite (GOES) is maintained on orbit in two ways. One is to let the spacecraft spin rapidly, which creates a gyro effect through the conservation of angular momentum, hence the name spin-stabilization. The other is to stabilize the spacecraft in all three dimensions, or 3-axis stabilization, such that the spacecraft appears truly stationary relative to the earth. Stabilization of a spacecraft has profound impact on every aspect of its mission, including the calibration of instrument onboard the spacecraft. Earlier GOES, as well as all the FY-2 and METEOSAT, was stabilized by spinning. Current GOES (starting with GOES-8) achieved 3-axis stabilization. This talk is a brief review of calibration experience during the transition of this major configuration change, including the expectation and preparation before the change and lessons learned after. It is hoped that the experience could help the preparation for GOES-R, another major advance in GOES history.

NOAA has considered flying an ocean color imager on the next series of GOES satellites to address its needs for data to assess the state of and manage coastal ecosystems and fisheries. The Coastal Ocean Applications and Science Team (COAST) was formed by NOAA to assess the need and utility of measuring coastal ocean color from a geostationary satellite. The first COAST experiment was conducted in Monterey Bay September 3-15, 2006. The goal of this experiment was to collect data that exceeds all possible requirements for a geostationary ocean color imager so that the data may be binned spatially or spectrally to create a simulated data set for any possible set of requirements. For the Monterey Bay experiment we used the Florida Environmental Research Institute's (FERI) Spectroscopic Aerial Mapper with On-board Navigation (SAMSON). SAMSON collects a full hyperspectral dataset covering 256 bands in the VNIR (3.5 nm resolution over 380 to 970 nm range) at 75 frames per second. It is designed with a Signal-to-Noise Ratio (SNR), stability, dynamic range, and calibration sufficient for dark target spectroscopy. Monterey Bay was sampled at 5 m Ground Sample Distance (GSD) as frequently as every 30 minutes. At the time of the COAST experiment there was a large Harmful Algal Bloom in the North-East corner of Monterey Bay. Here we use the SAMSON data to describe the short term dynamics of that bloom. Driven by tides and currents the bloom was seen to move kilometers on a time scale of hours. There is also evidence of vertical migration with the bloom concentrating on the surface near noon.